Analyzing administrative healthcare claims data and other data sources

ABSTRACT

Techniques suitable for identifying potential subjects for a clinical trial and other applications are disclosed. One or more exclusion or inclusion criteria are defined for the clinical trial. One or more specialized searching tables are pre-generated using administrative healthcare claims data and the one or more exclusion or inclusion criteria. The specialized searching tables are searched. Through the searching step, subjects are identified within the administrative healthcare claims data who match the one or more exclusion or inclusion criteria. Through the searching step, a geographical area is identified corresponding to the subjects who match the one or more exclusion or inclusion criteria. A customized report is generated using the identified subjects and geographical area.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of co-pending application Ser.No. 13/073,573, filed Mar. 28, 2011, which is a continuation of Ser. No.11/567,577, filed Dec. 6, 2006, and now issued as U.S. Pat. No.7,917,525 which claims the priority of U.S. Provisional PatentApplication 60/742,774, filed Dec. 6, 2005, the entire contents of eachof which are incorporated herein by reference in their entirety withoutdisclaimer.

COMPUTER PROGRAM LISTING APPENDIX

The computer program listing on the accompanying compact disk is herebyincorporated by reference (Artifact: 14/185476UA, Size: 34.7 KB,Created: Jul. 23, 2015).

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates generally to data mining and analysis.More particularly, it concerns mining and analyzing medial claims datato, e.g., (a) assist in the identification of clinical investigators andpotential trial subjects for clinical trials or determining feasibilityof clinical trials, (b) assist in the identification of medical expertwitnesses, medical directors, or other medical professionals, (c) assistin the investigation of medical fraud, and (d) assist in various typesof marketing. Even more particularly, it concerns improving the speed ofmedical-related data mining and analysis of very large data sets such asadministrative healthcare data through the creation and use ofspecialized searching tables (SSTs). It also concerns improving thespeed of certain statistical calculations through the creation and useof factorial tables having logarithmic entries, making it possible toreliably work with very large numbers and data sets.

2. Description of Related Art

A wealth of information is contained in administrative healthcare claimsdata. For example, an administrative healthcare claims database maycontain information concerning, but not limited to, patientidentification, physician identification, physician history,prescription drug history, medical examination history, medicaldiagnosis history, medical billing history, medical cost information,health benefit information, medical procedures, etc.

Conventional techniques have been employed to mine at least some of thisinformation. Data mining of healthcare claims data, however, involves aslow, computationally-intensive process that may return useful resultsonly after hours or more of computation time. Lengthy search andanalysis times plague the medical data mining field and discourage manyfrom fully utilizing medial claims data for useful applications.

Administrative Healthcare Claims Data and Statistical Calculations

Healthcare organizations and many other organizations lack the abilityto rapidly analyze extremely large data sets (e.g., over a billion claimlines), apply statistical analysis protocols, and aggregate output intorelevant, actionable answers for a specific need.

When working with very large datasets (like administrative healthcareclaims data), it is difficult and time consuming to look for patternsthat are non-random. Generally speaking, the process sometimes involvescomparing each record (for example in a claim) against every otherrecord, keeping track of differences, and then analyzing the differencesfor patterns. As data sets get larger, there can be an explosion in thenumber of unique comparisons that need to be made. For example, if onehas 10 million records, then adding one record may mean that there willbe 10 million new comparisons that need to be made and tracked. When onehas 100 million records and 1 record is added, there may be up to 100million new comparisons to make. As such, there are entire classes ofanalysis that are impractical or impossible to perform on very largedata sets, no matter how powerful the database engine.

Administrative Healthcare Claims Data for Clinical Trials

Clinical trials rely on voluntary participation of study subjects toevaluate new drugs, medical devices, or other interventions. Trials mayalso be directed to, among other things, evaluating procedures fordetecting or diagnosing a particular disease or finding ways to improvethe quality of life for those suffering from a chronic illness. Trialsare usually conducted by researchers associated in some way with apharmaceutical company, university, hospital, foundation, orgovernmental agency.

A significant challenge in carrying out any clinical trial is recruitingthe appropriate number and type of volunteer study subjects. Volunteerstudy subjects are selected so that they meet one or more exclusion orinclusion criteria defined by a study protocol that has been approved byan ethics review board. These criteria are aimed at investigating theimpact of a predefined intervention (e.g., a new drug) on a particularpatient population (e.g., include only hypertensive patients and excludethose younger than 18) and thereby characterize the effect of such anintervention on this population. This stage of the clinicaltrial—patient recruitment—can be costly, for each extra day it takes toidentify a pool of subjects may ultimately represent one fewer day a newdrug is on the market (and protected by a patent or other intellectualproperty). For some successful drugs, the cost of delay may approach oreven surpass millions of dollars per day.

Some have attempted to use administrative healthcare claims data for therecruitment of subjects for clinical trials. Services in existence todayinvolve researchers submitting a clinical trial protocol includingrelated inclusion and exclusion criteria to a data service. The dataservice accesses administrative healthcare claims data (often of limitedscope) in an attempt to estimate the size of a pool of potential studysubjects and estimate their location. The service, however, can takeupwards of one-month for results to be returned. This time delay comesabout, at least partially, due to the large amount of time necessary forthe actual data mining and analysis. Because healthcare claims data caninvolve millions of records, the searching necessary to identifypotential study subjects can be very time consuming and can, in someinstances, represent a significant time delay in bringing a drug tomarket. Additionally, the long delay may compound itself if researchersdiscover that a first set of inclusion/exclusion criteria would notyield a large enough potential study subject pool. When theinclusion/exclusion criteria are modified in an attempt to encompassmore participants, the researcher may be forced to wait another month orlonger before knowing if the change in criteria will indeed yield anappropriate number of possible study subjects.

Administrative Healthcare Claims Data for Detecting Medical Fraud

Data mining techniques known in the art have been used in an attempt todetect abnormalities in billing practices of physicians, throughanalysis of underlying claims data. For example, through claims data,one can attempt to determine whether there are any abnormalities orconsistent differences in billing practices that would result in higherpayments being directed to the physician in question.

Conventional techniques, however, suffer from the same or similarproblems discussed above—namely, lengthy analysis times. Additionally,because of the vast amount of data that may be associated with a claimsdatabase, traditional techniques have not been able to take advantage ofcertain statistical techniques that would provide particularly usefulinformation concerning potential fraud. For example, statisticaltechniques that employ the factorials of extremely large numbers are notundertaken at least because the calculations would cause “data overflow”errors, or other errors that would slow or stop an analysis.

Administrative Healthcare Claims Data for Other Applications

Mining administrative healthcare claims data for other applicationssuffers similar problems concerning long computation times and delay.The problems are believed to discourage researchers and others fromtaking advantage of the full potential of claims data.

The referenced shortcomings of conventional methodologies mentionedabove are not intended to be exhaustive, but rather are among many thattend to impair the effectiveness of previously known techniquesconcerning data mining and aggregated analysis of large amounts ofhealthcare claims data. Other noteworthy problems may also exist;however, those mentioned here are sufficient to demonstrate that themethodology appearing in the art have not been altogether satisfactoryand that a significant need exists for the techniques described andclaimed here.

SUMMARY OF THE INVENTION

Techniques disclosed here may be used to improve data mining andanalysis of administrative healthcare claims data. These techniques areapplicable to a vast number of applications, including but not limitedto (a) the identification of potential clinical trial investigators,identification of potential subject populations for clinical trialparticipation or analyzing the feasibility of clinical trials, (b) theidentification of medical expert witnesses, medical directors, or othermedical professionals, (c) the investigation of medical fraud, and (d)marketing. Medical research applications may also benefit from thetechniques of this disclosure. Although focused on administrativehealthcare claims data, the same techniques can be applied to othertypes of data.

In different embodiments, the techniques of this disclosure improve thespeed of data mining and analysis of administrative healthcare claimsdata through the creation and use of specialized searching tables(SSTs). The ability to use certain statistical calculations is provided.Further, those statistical calculations can be accomplished quicklythrough the creation and use of factorial tables including logarithmicentries, which make it possible to work with very large numbers and datasets. For example, hypergeometric statistical calculations can beperformed quicker using these tables than by traditional techniques.

In one respect, the invention involves a computerized method. One ormore exclusion or inclusion criteria are defined. One or morespecialized searching tables are pre-generated using the one or moreexclusion or inclusion criteria. The specialized searching tables aresearched. Through the searching step, data is identified within a dataset that matches the one or more exclusion or inclusion criteria.Through the searching step, a geographical area is identifiedcorresponding to the data that matches the one or more exclusion orinclusion criteria. A customized report is generated using theidentified data and geographical area. The method may also include (a)pre-generating one or more factorial tables, where the factorial tablesinclude logarithmic entries, (b) comparing one or more data recordsagainst a plurality of other records, and (c) calculating ahypergeometric statistical result based on the comparing step using theone or more factorial tables.

In another respect, the invention involves a computerized method foridentifying potential subjects for a clinical trial. One or moreexclusion or inclusion criteria are defined for the clinical trial. Oneor more specialized searching tables are pre-generated usingadministrative healthcare claims data and the one or more exclusion orinclusion criteria. The specialized searching tables are searched.Through the searching step, subjects are identified within theadministrative healthcare claims data who match the one or moreexclusion or inclusion criteria. Through the searching step, ageographical area is identified corresponding to the subjects who matchthe one or more exclusion or inclusion criteria. A customized report isgenerated using the identified subjects and geographical area. Definingone or more exclusion or inclusion criteria may include selectingcriteria using a Venn diagram. Defining one or more exclusion orinclusion criteria may include selecting one or more medical diagnosiscodes. Identifying the geographical area may include identifying a zipcode. The customized report may include a map illustrating subjectsaccording to location. The method may also include identifying potentialclinical investigators for the clinical trial through searching of thespecialized searching tables and generating a customized report usingidentified investigators and a corresponding geographical area. One ormore investigator databases may be used to identify the investigators.The method may also include, prior to the generating of the customizedreport, defining a minimum subject participation and modifying the oneor more exclusion or inclusion criteria if the number of subjects withinthe administrative healthcare claims data who match the one or moreexclusion or inclusion criteria does not meet the minimum subjectparticipation. Such modifying may be done automatically. Such modifyingmay be done automatically and iteratively until the minimum subjectparticipation is met. This technology may be embodied on a computerreadable medium comprising computer executable instructions that, whenexecuted, carry out the techniques described here.

In another respect, the invention involves a computerized method forrecruiting a medical professional. One or more exclusion or inclusioncriteria are defined for the medical professional. One or morespecialized searching tables are pre-generated using administrativehealthcare claims data and the one or more exclusion or inclusioncriteria. The specialized searching tables are searched. Through thesearching step, medical professionals are identified within theadministrative healthcare claims data who match the one or moreexclusion or inclusion criteria. Through the searching step, ageographical area is identified corresponding to the medicalprofessionals who match the one or more exclusion or inclusion criteria.A customized report is generated using the identified medicalprofessionals and geographical area. Defining one or more exclusion orinclusion criteria may include selecting criteria using a Venn diagram.Defining one or more exclusion or inclusion criteria may includeselecting one or more medical diagnosis codes. The medical professionalsmay include physicians being recruited as an expert witness forlitigation. The method may also include determining if one or more ofthe physicians have previous experience as an expert witness, throughcorrelation with one or more expert databases. This technology may beembodied on a computer readable medium comprising computer executableinstructions that, when executed, carry out the techniques describedhere.

In another respect, the invention involves a computerized method forstatistical calculations based on administrative healthcare claims data.Administrative healthcare claims data is searched. One subset of theadministrative healthcare claims data is compared against a plurality ofother subsets of the administrative healthcare claims data. Ahypergeometric statistical result is calculated based on the comparingstep using one or more pre-generated factorial tables, the factorialtables including logarithmic entries. Calculating may include one ormore calculations using the logarithmic entries followed by one or moreexponential operations. The method may also include using thehypergeometric statistical result to detect medical-related fraud. Theone subset may include medical coding data associated with a firstphysician and the plurality of other subsets may include medical codingdata associated with a plurality of other physicians. The plurality ofother physicians may be selected to be within the same specialty as thefirst physician. The method may also include generating a customizedreport comparing the first physician versus the plurality of otherphysicians. The customized report may include a graph of utilizationpercentage versus medical code for the first physician and the pluralityof other physicians. The method may also include using thehypergeometric statistical result to rate one physician versus otherphysicians. The method may also include using the hypergeometricstatistical result to identify potential subjects for a clinical trial.The method may also include using the hypergeometric statistical resultto recruit a medical professional for use as an expert witness forlitigation. This technology may be embodied on a computer readablemedium comprising computer executable instructions that, when executed,carry out the techniques described here.

In another respect, the invention involves a computerized method, inwhich one or more specialized searching tables are pre-generated usingadministrative healthcare claims data. One or more factorial tables arepre-generated, the factorial tables including logarithmic entries. Thespecialized searching tables are searched. Through the searching step,one or more records are identified within the administrative healthcareclaims data that matches one or more search criteria. The one or morerecords are compared against a plurality of other records of theadministrative healthcare claims data. A hypergeometric statisticalresult is calculated based on the comparing step using the one or morefactorial tables. A customized report is generated using the one or morerecords and the statistical result. The one or more search criteria mayinclude one or more exclusion or inclusion criteria selected using aVenn diagram. The calculating may include one or more calculations usingthe logarithmic entries followed by one or more exponential operations.This technology may be embodied on a computer readable medium comprisingcomputer executable instructions that, when executed, carry out thetechniques described here.

As used in this disclosure, an “inclusion criteria” means a parameterthat aims at including certain data in search results. An “exclusioncriteria” aims to exclude certain data in search results. Inclusion andexclusion criteria are relative terms—an inclusion criteria may bynecessity exclude some data and vice-versa. In general, an exclusion orinclusion criteria is simply a searching parameter. Specifically,exclusion or inclusion criteria can be any parameters that define asearch and operate to filter or potentially filter data.

As used in this disclosure the term, “pre-generate” means to generateprior to any searching step.

As used in this disclosure the term, “Specialized Searching Table” or“SST” means a custom, indexed data table organized according topredefined exclusion or inclusion criteria, the indexed table populatedwith a subset of information from one or more larger tables. The SST isdesigned to optimize or speed the searching of data, at the expense ofadded disk space or other memory, for it reproduces a subset ofinformation from one or more larger tables into a separate table that isthen searched. One SST can act in concert with one or more other SSTs toachieve a search. Searching of SSTs can be done in parallel, serially,or a combination thereof. In one embodiment, an SST or set of SSTs maybe built with or on a FACT table using a concatenated index (an indexcontaining several fields and leading with the appropriate field(s)). Insuch an embodiment, optimal queries only use the SST index structure andnot interact with the FACT table. In this disclosure, SSTs may also bereferred to as “packed” tables.

As used in this disclosure, “administrative healthcare claims data” or“healthcare data” is used according to its ordinary meaning in the artand should be interpreted to include, at least, data organizedelectronically that is searchable via computer algorithm and whichcontains records associated with one or more medical procedures,prescriptions, diagnoses, medical devices, etc.

As used in this disclosure, “match” in the context of a search should beinterpreted to include exact matches as well as substantial matches ormatches set up with a pre-defined tolerance.

As used in this disclosure the term, “customized report” means an output(hard-copy or soft-copy) that is individually tailored for the user(e.g., person or entity) through the inclusion of a result or resultsummary prompted through user input. A customized report need not beunique to a user.

As used in this disclosure the term, “minimum subject participation” isany quantitative measure of a minimum level of participation such assubject total or subject density.

As used in this disclosure the term, “factorial table” is an indexeddata table whose entries include factorial values for one or morenumbers. In a preferred embodiment, a factorial table is an indexed datatable whose entries include logarithmic representations of factorialvalues for one or more numbers.

The term “code keys,” as used herein, represents any desired searchableattribute. In one embodiment, “code keys” may represent diagnosis codes,prescription codes, procedure codes, or medical device codes.

The terms “a” and “an” are defined as one or more unless this disclosureexplicitly requires otherwise.

The term “approximately” and its variations are defined as being closeto as understood by one of ordinary skill in the art. In onenon-limiting embodiment the terms are defined to be within 10%,preferably within 5%, more preferably within 1%, and most preferablywithin 0.5%. The term “substantially” and its variations are defined asbeing largely but not necessarily wholly what is specified as understoodby one of ordinary skill in the art. In one non-limiting embodiment theterms refer to ranges within 10%, preferably within 5%, more preferablywithin 1%, and most preferably within 0.5% of what is specified.

The terms “comprise” (and any form of comprise, such as “comprises” and“comprising”), “have” (and any form of have, such as “has” and“having”), “include” (and any form of include, such as “includes” and“including”) and “contain” (and any form of contain, such as “contains”and “containing”) are open-ended linking verbs. As a result, a method ordevice that “comprises,” “has,” “includes” or “contains” one or moresteps or elements possesses those one or more steps or elements, but isnot limited to possessing only those one or more elements. Likewise, astep of a method or an element of a device that “comprises,” “has,”“includes” or “contains” one or more features possesses those one ormore features, but is not limited to possessing only those one or morefeatures. Furthermore, a device or structure that is configured in acertain way is configured in at least that way, but may also beconfigured in ways that are not listed.

The term “coupled,” as used herein, is defined as connected, althoughnot necessarily directly, and not necessarily mechanically.

Other features and advantages will become apparent with reference to thefollowing detailed description of specific, example embodiments inconnection with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The following drawings form part of the present specification and areincluded to further demonstrate certain aspects of the presentinvention. The drawings do not limit the invention but simply offerexamples.

FIG. 1 is a flowchart showing a computerized method for identifyingclinical trial investigators and potential subject populations for aclinical trial, for recruiting a medical professional, or for evaluatingthe feasibility of a clinical trial, in accordance with embodiments ofthe invention. The steps of FIG. 1 can also be used for otherapplications.

FIG. 2 is a flowchart showing a computerized method for statisticalcalculations based on administrative healthcare claims data, inaccordance with embodiments of the invention. The steps of FIG. 2 canalso be used for other applications.

FIG. 3 is a schematic diagram of a computer system including a computerreadable medium suitable for carrying out techniques of this disclosure,in accordance with embodiments of the invention.

FIGS. 4A-4C are schematic diagrams of a computer software interfacesuitable for carrying out techniques of this disclosure, in accordancewith embodiments of the invention.

FIG. 5 is a list of example indications that can make up aadministrative healthcare claims data search criteria, in accordancewith embodiments of the invention.

FIG. 6 is a schematic diagram illustrating how one or more exclusion orinclusion criteria can be selected using a Venn diagram, in accordancewith embodiments of the invention.

FIG. 7 is a schematic diagram illustrating example customized reports,in accordance with embodiments of the invention.

FIGS. 8-9 are map-based customized reports, in accordance withembodiments of the invention.

FIG. 10 is a customized report directed at fraud detection, inaccordance with embodiments of the invention.

DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

Embodiments of this disclosure allow for the computerized identificationof clinical trial investigators and potential subject populations for aclinical trial, the computerized identification of medical professionals(e.g., as an expert witness for litigation, as a medical director forlarge hospitals), determining the feasibility of a clinical trial,marketing, and other purposes. Embodiments of this disclosure also allowfor the improved calculation of statistical results using, e.g.,pre-generated tables and transforming factorial tables into theirlogarithmic equivalent. The statistical results can be used to furtherefforts for recruiting, marketing, and other applications.

Turning first to FIG. 1, an example method 100 is shown for identifyingclinical investigators and potential subject populations for a clinicaltrial, determining the feasibility of a clinical trial, or recruiting amedical professional.

Defining Exclusion/Inclusion Criteria

In step 102 of FIG. 1, one or more exclusion or inclusion criteria aredefined. In a preferred embodiment, the exclusion or inclusion criteriaare designed to correspond to criteria for a clinical trial or otherapplication such as recruiting a medical professional and may include,but are not limited to, desired characteristics of a person (e.g., age,gender, etc.), a targeted health condition (e.g., possessing a certaindiagnosis or being associated with a medical diagnosis code, etc.), oran employment characteristic (e.g., medical specialty, etc.). In apreferred embodiment, exclusion or inclusion criteria are definedthrough direct input from a user. In other embodiments, criteria may beinput from other software (e.g., a parameter may be generated insoftware and output for use in a search). In still other embodiments,criteria may be pre-stored and loaded or otherwise accessed.

FIGS. 4A-4B illustrate some possible ways in which exclusion orinclusion criteria may be defined via a computer software interface,with direct user input. FIG. 4A presents a “wizard” environment, inwhich a user is asked to enter exclusion/inclusion criteria by typingone or more parameters along with a respective operator and value.

Criteria may be keywords specifically recognized by software, as shownin FIG. 4A, in which software recognizes the terms “Gender,”“Condition,” and “Age.” In the illustrated embodiment, “Gender” refersto male/female, “Condition” refers to a recognized medical condition,and “Age” refers to the age of a person. In other embodiments, suitablecriteria (and optionally, here, medical conditions) may be chosenthrough a “pull down” or “drop down” list or other mechanism known inthe art. For example, one may be presented with a link stating, “Listpossible criteria.” Clicking this link would allow the user to view alist of criteria along with an explanation about each. In differentembodiments, the list of criteria may be modifiable by the user,depending on application and depending on the underlying data beingsearched. For example, if access is provided to a database that has oneor more new fields available for search, one may want to add additionalsearch criteria based on those fields. In the embodiment of FIG. 4A,software allows one to enter up to five criteria. In other embodiments,more or fewer may be provided. In other embodiments, the user mayspecify the number of criteria desired for a particular application. Inone embodiment, entire groups of criteria may be defined by a“one-click” or other shortcut manner by, e.g., providing a button ormenu that allows a user to define groups of criteria by shortcut name,or through reference to previously-used or saved groups of criteria. Ifa parameter is not recognized, an appropriate alert or error message maybe generated.

Operators suitable for use in the illustrated embodiment of FIG. 4Ainclude, but are not limited to, an equal sign (=), the greater-thansign (>), and the less-than sign (<). These operators act on, or modifythe “value.” In other embodiments, the operator can be any mathematicalor logical operator known in the art to assist in searching. Forexample, standard or customized Boolean-type operations may bepermitted. As shown in FIG. 4A, the operator for the first and secondcriteria is the equal sign (=), and the operator for the final parameteris the less-than-or-equal-to combination (<=). If an operator is notrecognized, an appropriate alert or error message may be generated.

The “value,” acting with the operator and criteria, establishes whatsearch is to be performed. In FIG. 4A, the first, second and thirdsvalues are, respectively, Female, Diabetes, and 35. Accordingly, thetype of search the user may be interested in involves people who arefemale, are associated with the Diabetes medical condition, and who are35 years old or younger. In one embodiment, values are entered directlyby a user. In other embodiments, values may be pre-stored and loaded foruse, selected individually or in groups, or otherwise entered. FIG. 5 isa list of example indications that can act as a value for a criteriasuch as “Condition,” which is shown in FIG. 4A. More or fewerindications could be used in other embodiments.

FIG. 6 indicates another example method by which one may define a valuefor a criteria—here, through the use of accepted medical codes ormedical diagnosis codes, such as IDC9 codes. Specifically, to establisha value for a criteria such as “Condition,” one may enter one or moreIDC9 codes to help identify what condition is of interest. In theembodiment of FIG. 6, eight different IDC9 diagnosis codes are beingused to define an HIV exclusion or inclusion criteria, which isrepresented by the upper left circle of the Venn diagram of FIG. 6. Indifferent embodiments, software may help users look-up appropriate codesto aid in the searching process. For example, if a user wants a searchto involve hepatitis, one may look-up all medical diagnosis codespertinent to all forms of hepatitis. The user may then select from thelook-up results to define exclusion or inclusion criteria, andparticularly, values for condition-related criteria.

A Venn diagram or other technique may be used to help the user define orvisualize exclusion or inclusion criteria. FIG. 4B illustrates using aVenn diagram for this purpose. The Venn diagram of FIG. 4B is used inconjunction with defining exclusion or inclusion criteria and appears ina “wizard” screen that is called once the user selects “Next” aftersetting up criteria, operators, and values in FIG. 4A. The Venn diagramof FIG. 4B includes three circles, corresponding to the criteriapreviously defined in FIG. 4A and an additional two studies or groups.In this embodiment, each complete list of inclusion/exclusion criteriacreates one Venn diagram. The Venn diagrams allow users to overlapmultiple studies or groups. Thus, the first circle in FIG. 4Bcorresponds to the entire set of criteria listed in FIG. 4A—Females withDiabetes age 35 or younger. The second circle in FIG. 4B wouldcorrespond with another study—for example, people with hypertension andhepatitis. The third circle in FIG. 4B would correspond with yet anotherstudy, trial, or protocol—for example, children under the age of 12 whohave received gamma. The Venn capability provides the ability toidentify clinical investigators and potential patient populations whoreside in the intersection of these three separate studies. The resultmay therefore be a list of providers who treat one or more patients whoare female with diabetes who have hypertension and have had hepatitiswho are under 12 and have received a gamma shot. This ability allowsusers to establish completely separate protocols for different drugs andto combine protocols in the future for new drugs and/or differentindications (potentially identifying off label use, etc.)

Of course, a different number of criteria would lead to a different Venndiagram, with different labels. The Venn diagram allows the user totailor a search according to any of the exclusion or inclusion criteriaalone or in any combination with other exclusion or inclusion criteria.In the illustrated embodiment of FIG. 4B, there are seven differentpossibilities for searching, represented by the seven differentcheckboxes presented to a user. Here, by way of example only, the userhas selected a search aimed at uncovering data that satisfies the Gendercriteria (i.e., Female), the “Condition” criteria (i.e., HIV), and theAge criteria (i.e., 35 years old or younger). Had the user wished tosearch a different combination, he or she could have checked a differentbox. Additionally, a user may wish to chose more than one box todetermine, e.g., the difference in the number of search “hits” thatwould result if different exclusion or inclusion criteria combinationsare considered. If more than one box is checked, search output may bearranged or formatted to indicate the search results corresponding toeach check box.

FIG. 6 illustrates another Venn diagram that can be used to assist insetting up exclusion or inclusion criteria. There, the criteria involvedare similar to those of FIG. 4B—they are HIV, age greater than 45, andFemale. Five of the seven possible combinations of criteria arenumbered. In FIG. 6, different medical codes associated with HIV aregiven at left (e.g., code 042 for HIV). In FIG. 6, the numbers inparenthesis (e.g., 23718) are counts in the searched member populationthat have the particular Dx/Rx/Px.

In one embodiment, the exclusion or inclusion criteria may be chosen tosatisfy conditions of a clinical trial so that one may recruit subjects(e.g., so that one may, through the searching process, identify patientswho would meet the clinical trial criteria). For example, if aresearcher is recruiting patients for a drug study and desires volunteerpatients over the age of 40 who have asthma but who are not taking aparticular class of blood-pressure medications, those criteria may beentered.

In one embodiment, the criteria may be model criteria, chosen by theresearcher simply to see if there would be a suitable subject pool ifthe model criteria were, in fact, actual requirements. In other words,criteria may be set up to model a clinical trial for potential subjectidentification. Such modeling may be used to provide a list of potentialsuggestions that could be implemented to meet clinical trial enrollmenttargets. Such modeling, discussed more below, may also allow a user tocheck on whether an investigator's enrollment predictions seemreasonable as well as provide temporal and geographic data on targetedenrollment. Additionally, modeling may allow a user to evaluate whether,based on patient base attrition, an investigator is likely to retainstudy trial subjects.

In another embodiment, the exclusion or inclusion criteria may be chosento satisfy job conditions so that one may recruit a medicalprofessional. For example, one may define exclusion or inclusioncriteria to find a suitable medical expert witness for litigation. Iflitigation involves esophagus injuries associated with screws backingout from an anterior cervical plate, one could define exclusion orinclusion criteria designed to locate a surgeon who has performed over100 cervical plate procedures during the past five years. If onebelieves that a female expert would “connect” more with the jury, onecould define a Gender criteria to be equal to Female. If one believesthat the expert witness should be from Texas, one could set a MedicalSchool criteria to be equal to one or more Texas schools. If onebelieves that an expert in the 45-65 age range would have the mostcredibility, an age criteria could be entered accordingly. In the samemanner, one could tailor a search according to any desire, and limitedonly by the underlying data being searched. As with the clinical trialrecruitment embodiment, one may define exclusion or inclusion criteriato simply satisfy different “what if” scenarios—for example, “what if” Iwas looking for a male expert witness, age 52-55, who went to BaylorCollege of Medicine, and who has done over 400 cervical plateprocedures—how many such people could I possibly identify? If the answeris zero or extremely low, one may realize that expectations need to bemodified.

In another embodiment, the exclusion or inclusion criteria may be chosento satisfy job conditions so that one may recruit a medical professor,executive, researcher, etc. For example, one may define exclusion orinclusion criteria to find an executive with particular experience as aphysician working with certain conditions.

These examples illustrate that it may be beneficial to combine data fromone database with that of others so that additional criteria may bedefined and used for various applications. For example, in the clinicaltrial recruitment applications, it may be beneficial to use informationthat identifies physicians as being past investigators for clinicaltrials so that one may identify not only volunteer study subjects, butalso appropriate physicians with experience with trials. This may beaccomplished by linking administrative healthcare claims databases with,for instance, an FDA-related database. Additionally, one may identifymedical professionals who have testified at trial or deposition bycorrelating a physician match from a administrative healthcare claimsdatabase with a database that keeps track of expert witness experience.

Pre-Generating Specialized Searching Tables

In step 104 of FIG. 1, specialized searching tables (SSTs) arepre-generated. The SSTs of the present disclosure offer significantbenefits in the area of administrative healthcare claims data searchingas well as other fields at least because of the marked improvement insearching speed and any associated analysis—albeit at the expense ofusing more disk space (or other computer memory) and the time associatedwith pre-generating the tables themselves, which may be done at off-peaktimes, if desired. In one embodiment, over 18 million patient healthcareclaims histories, resulting in over 410 million records, may be “packed”into SSTs to greatly improve data mining and analysis.

In one embodiment, SSTs may be pre-generated and used as follows. Inthis example, “code keys” represent any desired searchable attributeincluding, but not limited to, Diagnosis codes, Prescription codes,Procedure Codes, etc. In this example, temporal information may also beutilized (e.g., service date) to define encounters in the data set.Those having ordinary skill in the art, having the benefit of thisdisclosure, will recognize that other types of information may beincluded for SSTs, according to need. The steps below represent anexample only.

Creating SSTs

1. CREATE TABLE PACKED_TABLE as SELECT or INSERT /*+append*/ . . .select distinct code_key, sex, birth date, geographic region,individual_id from fact table of other large table . . . . ORDER BYcode_key, sex, birth date, geographic region, individual_id settingappropriate block parameters to 0 space for updates

2. Index the PACKED_TABLE by code_key

-   -   a. alternate 1: create concatenated index on large table leading        with code_key and containing all required fields.    -   b. Alternate 2: create stand alone index organized table leading        with code_key and containing all required fields.        Access SSTs

1. set#1 is SELECT sex, birth date, geographic region, individual_idfrom PACKED_TABLE where code_key in (code_key1a,code_key2a,etc)

2. set#2 is SELECT sex, birth date, geographic region, individual_idfrom PACKED_TABLE where code_key in (code_key1b,code_key2b,etc)

3. set#N is SELECT sex, birth date, geographic region, individual_idfrom PACKED_TABLE where code_key in (code_key1X,code_key2X,etc)

4. The sets can then be combined via INTERSECT, UNION, MINUS, etc. toyield results corresponding to any/all Venn region(s). Patientdemographic summaries can also be calculated rapidly without requiringjoins.

The SSTs of this disclosure can overcome a number of performanceobstacles in both the gathering and the processing of large data setsfor, e.g., real time statistical probability analysis (a.k.a. signaldetection). This method can also contain temporal information (e.g.,service date) to define encounters in the data set. Standard warehousestructures hold vast amounts of data and allow access to specificrecords via bitmapped indexes. However, when large population sets aredesired, the shear number of disk seeks required via the bitmappedindexes becomes prohibitive for real-time processing. A solutionprovided herein is to use SSTs (or standard tables loaded and indexed ina particular way) where each block of each table is rich with thedesired information. In other words, if one is searching for possiblepatients who have had at least one of a set of 10 diabetic medicalcodes, the user may be directed to a table which contains rows packed bycodes. Each physical block read (disk seek) may contain hundreds of thedesired individuals, whereas in a standard warehouse a block read willcertainly hold at least one desired individual but likely, at most, onlya few as dictated by chance.

The SSTs of this disclosure can also overcome statistical processingchallenges present in traditional data mining operations. Statisticalprocessing challenges can be encountered once individuals “in play” areferreted out. For example, if checking for drug safety signals, eachattribute of each individual “in play” must be accessed. Also, eachoutcome for these individuals must be accessed. These two sets may thenbe permeated against each other, one individual at a time. This processis repeated for each “in play” individual, and the cumulative set isthen aggregated for each outcome for each base condition, for each drugin the pairing. For weak filters (filters than don't appreciably narrowthe population) this can be a time consuming process. To alleviate this,and according to embodiments of this disclosure, all possiblepermutation sets may be pre-generated and “packed” by individual IDsinto an SST. The “in play” population may then be extracted from thepre-generated SST for aggregation.

Techniques of this disclosure may be advantageously applied to a widevariety of “raw data” to be searched, a preferred embodiment involvingadministrative healthcare claims data. For example, SSTs can act onvirtually any data to improve searching and analysis, and particularlyadministrative healthcare claims data, regardless of the format and sizeof the data being mined. In one embodiment, administrative healthcareclaims data may be housed on computer servers or other storage devicesat one physical location, while in other embodiments, the data may bedispersed about many locations. The data may be accessible via network.The data may be in one or more different formats or layouts.Advantageously, the techniques of this disclosure can lay on top ofvirtually any data, and the data can be linked together from a varietyof sources. Because of inherent TCP transport delays, when dealing withlarge amounts of data spread across multiple platforms, there is aperformance advantage to ensuring that each platform's SST data set beself contained in the sense that only aggregated values are passed to anapplication server, client or master database server. SSTs havesignificant performance benefits whenever data sets are primarilyaccessed by a non-unique field.

SSTs can be updated in a number of ways. In one embodiment, thisactivity may be done off hours. Updating may be done as follows, whichare examples only:

1. Completely reprocess the SST from a FACT table each load cycle.

2. INSERT /*+APPEND*/ the new data into the existing table in thedesired order. This will not “pack” as tightly as a complete reprocessbut will still have most of the performance advantages provided by thisdisclosure.

Significant storage benefits can be reaped if the database in use allowsfield compression on leading and even non-leading index fields.Depending on the packing method used, indexes may be dropped prior toloading and re-created post-load. Or, indexes may be allowed to growduring the load process. However, repeated loads (and deletions) canhave detrimental effects on index efficiency.

Searching the SSTs

In step 106 of FIG. 1, the SSTs are searched. The searching stepinvolves the use of the SSTs to filter the underlying administrativehealthcare claims data (or other data being searched) according to theexclusion or inclusion criteria defined by the user, and which may beassociated with a Venn diagram or other tool. The searching step itselfmay be carried out according to techniques known by those of ordinaryskill in the art.

In one embodiment, searching may be carried out using the techniques ofthe following example. In this example, one might be interested indetermining how many physicians have treated a specific set of diagnosesin a certain way during an encounter (with a specific class of drugs andspecific set of procedures) and how many patients each physician hastreated in that way, total encounters by physician. This or a similarembodiment may be framed as shown in the following non-limitingscenarios:

1. One class of search only requires that the patient have had certainDx, Px and Rx codes during an interval regardless of the intervalsbetween the codes (e.g., in the last year which patients have hadprocedure “A” and drug “B”).

2. Another class of search imposes temporal restrictions on the orderand interval between the Dx, Px and Rx codes. A temporal example mightbe: A novel treatment approach for disease “X” (coded as x1, x2, or x3)is procedure “Y” (coded as y1 or y2) and drug “Z” (coded as z1 or z2).One may define this novel treatment as belonging to an “encounter,”which may require the diagnosis “X” to occur on or before “Y” and “Z,”and furthermore, “Y” must take place on at most 1 day after “X,” and “Z”must be filled on or at most 2 days after “X”. Now, one may find allpatients who have had disease “X” in any of its x1, x2 or x3 forms whowere treated with procedure “Y” in forms y1 or y2 within 1 day andfilled drug “Z” in forms z1 or z2 within 2 days.

3. This logic can be further extended into an “episode” where theprocedure “Y” might have a much longer interval of treatments and evennumerous treatments over this longer interval (same with drug “Z”).

Regardless of the logic, an output common to all three may be, in oneembodiment: Count the unique patients that fit this logic, identify andcount the providers that treat patients this way; which providers dothis treatment the most often, and which providers do not treat patientsthis way.

One might also be interested in the demographics of the patientpopulation that has participated in three code sets even if they did nothappen in the same encounter. In this example one may create two SSTstructures (this could be done in one denormalized SST with a moderateperformance hit due to increased row length).

Some fields in this example are shown with “natural” values although itis generally desirable to use surrogate keys of the smallest possiblelength for the desired criteria. After aggregation, the surrogate keysmay be joined to descriptive fields.

Demographics of the patient population that has participated in allthree code sets even if they did not happen in the same encounter:

SST#1 code_key, birth date, geo region, sex, individual_id Ordered bycode_key indexed by code_key -- tally by gender breakout, age and regionare similar Select sex, count(unique individual_id) unique_patients from( Select birth date, geo region, sex, individual_id from SST#1 where dxIN (Dx1,Dx2,...) INTERSECT Select birth date, geo region, sex,individual_id from SST#1 where px IN (Px1,Px2,...) INTERSECT Selectbirth date, geo region, sex, individual_id from SST#1 where px IN(Rx1,Rx2,...) ) group by sex

Note: when available, temporary holding structures (e.g., global temptables, WITH temp AS, etc.) can avoid multi-sourcing. Analytic functionscan also be used.

Example of Multi-Sourcing from a Single Temp Structure

WITH temp AS ( SELECT /*+ first_rows*/ b.*  FROM (SELECT /*+ index(a)*/individual_id  FROM MASTER_R1D a  WHERE a.code_key IN (′DX8648571′,′DX864857481′, ′DX864857491′, ′DX864857501′, ′DX864857511′,′DX864857521′, ′DX864857531′, ′DX86485753481′, ′DX86485753491′,′DX864857541′, ′DX864857551′, ′DX86485755481′, ′DX86485755491′,′DX864857561′, ′DX86485756481′, ′DX86485756491′, ′DX864857571′,′DX86485757481′, ′DX86485757491′, ″ ) INTERSECT SELECT /*+ index(a)*/individual_id  FROM MASTER_R1D a  WHERE a.code_key IN (′PX48485354541′,′PX51515349481′, ′PX51515349491′, ′PX51515349501′, ′PX51515349511′,′PX51515349521′, ′PX51515349541′, ′PX51515349551′, ′PX51515349561′,′PX51515349571′, ′PX51515350491′, ′PX51515350501′, ′PX51515350511′,′PX51515351511′, ′PX51515351521′, ′PX51515351531′, ′PX51515351541′, ″ )INTERSECT SELECT /*+ index(a)*/ individual_id  FROM MASTER_R1D a  WHEREa.code_key IN (′RX-ZITHROMAX′, ″)) a, INDIVIDUAL_ID_R1D b WHEREa.individual_id = b.individual_id) SELECT −1 ord, ′Male′ ATTRIBUTE, SUM(CASE WHEN gender = ′M′ THEN 1 ELSE 0 END) counts  FROM temp UNION ALLSELECT −2 ord, ′Female′ ATTRIBUTE, SUM (CASE WHEN gender = ′F′  THEN 1ELSE 0  END) counts  FROM temp UNION ALL SELECT *  FROM (SELECT rn,rn_chr ATTRIBUTE, counts  FROM (SELECT TO_CHAR (TRUNC ((SYSDATE − dob) / 365.25) ) ATTRIBUTE, COUNT (UNIQUE CASE WHEN gender = ′M′ THENindividual_id ELSE NULL END ) counts FROM temp   GROUP BY TO_CHAR (TRUNC((SYSDATE −  dob) / 365.25))) a,  YEARS b  WHERE b.rn_chr =a.ATTRIBUTE(+) ORDER BY b.rn ASC) UNION ALL SELECT *  FROM (SELECT rn +500, rn_chr ATTRIBUTE, counts  FROM (SELECT TO_CHAR (TRUNC ((SYSDATE − dob) / 365.25) ) ATTRIBUTE, COUNT (UNIQUE CASE WHEN gender = ′F′ THENindividual_id ELSE NULL END ) counts FROM temp  GROUP BY TO_CHAR (TRUNC((SYSDATE −  dob) / 365.25))) a,  YEARS b  WHERE b.rn_chr =a.ATTRIBUTE(+) ORDER BY b.rn ASC) UNION ALL SELECT *  FROM ((SELECT 1000rn, ′ZIP-3:′  ∥ SUBSTR (zipcode, 1, 3)  ∥′ ′  ∥ TRIM (city)  ∥ ′,′  ∥state ATTRIBUTE,  COUNT (UNIQUE individual_id) counts  FROM temp, ZIP3 WHERE SUBSTR (zipcode, 1, 3) = ZIP3 GROUP BY 1, ′ZIP-3:′  ∥ SUBSTR(zipcode, 1, 3)  ∥ ′ ′  ∥ TRIM (city)  ∥ ′,′  ∥ state) ORDER BY countsDESC);

Determining how Many Physicians have Treated a Specific Set of Diagnosisin a Certain Way During an Encounter (with a Specific Class of Drugs andSpecific Set of Procedures) and how Many Patients Each Physician hasTreated in that Way, Total Encounters by Physician

SST#2 code_key, individual_id, encounter date, provider_key ordered bycode_key indexed by code_key Select provider_key,count(uniqueindividual_id) unique_patients,count(uniqueindividual_id||encounter_date) total_encounters from ( Selectindividual_id, encounter date, provider_key from SST#2 where dx IN(Dx1,Dx2,...) INTERSECT Select individual_id, encounter date,provider_key from SST#2 where px IN (Px1,Px2,...) INTERSECT Selectindividual_id, encounter date, provider_key from SST#2 where px IN(Rx1,Rx2,...) ) group by provider_key

In one embodiment, SSTs may also work together. Consider a query whereone wants to know the ensuing “play-out” after an individual has had aparticular Diagnosis, procedure or drug:

Table SST#3 is code_key, individual_id, encounter_date ordered bycode_key,individual_id and indexed by code_key

Table SST#4 is individual_id,encounter_date,code_key ordered byindividual_id, encounter_date and indexed by individual_id

Then the combined query becomes (one of ordinary skill in the art willrecognize that they are many ways to write the SQL):

Select SST#4.* from SST#4,(Select individual_id,min(encounter_date)first_encounter from SST#3 group by individual_id) iv

where SST#4.individual_id=iv.individual_id andSST#4.encounter_date>iv.first_encounter

Searching times of course depend on the amount of data involved.However, using SSTs can dramatically cut searching time to a degreewhere once-impossible or impracticable tasks can be completed—e.g.,identification of clinical investigators and potential subjectpopulations for clinical trials in a quick enough manner so thatexclusion or inclusion criteria can be modified “on the fly” in anattempt to establish appropriate protocols with a feasible subject pool.

To highlight performance difference versus existing warehousetechniques, consider the following example query:

Select sex, count(unique individual_id) unique_patients from ( Selectbirth date, geo region, sex, individual_id from SST#1 where dx IN(Dx1,Dx2,...) INTERSECT Select birth date, geo region, sex,individual_id from SST#1 where px IN (Px1,Px2,...) INTERSECT Selectbirth date, geo region, sex, individual_id from SST#1 where px IN(Rx1,Rx2,...) ) group by sex

To accommodate itemized costs by procedure and the fact that hundreds ofprocedures may sometimes define an encounter, administrative healthcareclaim tables generally contain a single procedure per line with otherfields holding diagnosis codes or pointers to a table listing thediagnosis codes associated with the encounter. Prescription data isoften, but not always, held in a separate table. Bitmapped indexes arenot tailored to this class of problem because they cannot be directlymerged/ANDed to narrow the output set prior to table access because thecodes are not required to be resident on the same line, only on the samepatient. Building a claim table holding every triplet permutation overthe covered patient interval could leverage bitmapped index power butwould be cause the table to be prohibitively large for anything but asmall subset of the population. Likewise “x-walking” columns into asingle table is not practical when hundreds of codes are possible in ancovered period.

So, for a generic claims table containing

-   -   Individual_id, Px1, Dx1, Dx2, Dx2, etc.

And pharmacy table containing

-   -   Individual_id, Rx1, etc

The warehouse version of the query becomes:

Select p.sex,count(unique individual_id) unique_patients from ( Selectindividual_id from big_claim_table where px1 in (Px1,Px2,...) -- we mustpull this set independently since they may not occur on the sameline/encounter as the Dx codes INTERSECT Select individual_id frombig_claim_table where (dx1 IN (Dx1,Dx2,...) OR dx2 in (Dx1,Dx2,...) ORdx3 in (Dx1,Dx2,...) INTERSECT Select individual_id frombig_pharnmacy_table where rx1 in (Rx1,Rx2,...) ) iv ,patient_lu p whereiv.individual_id=p.individual_id group by sex

This query run on modest code sets compares as follows:

Data Structure Physical I/Os Time to run Warehouse 400000 1000 secondsSST 3300  10 seconds Or 100 times faster.Identifying Patients, Professionals, Geography, and Other Results

In step 108 of FIG. 1, searching yields an identification of datamatching the exclusion or inclusion criteria previously defined. Anydata fields, portions of data fields, or combinations of data fields maybe identified in response to the search. In one embodiment, theidentification of data comes about through an exact match with searchcriteria. In other embodiments, a search “hit” may result if there is anapproximate or substantial match. For instance, if an exclusion orinclusion criteria seeks physicians who have treated 200 patients havinga particular diagnosis, one embodiment would return information forphysicians who have treated, e.g., 195 patients. Non-exact matches suchas these can be indicated accordingly on a customized report. In oneembodiment, the tolerance required to constitute a match may be definedby the user, and the tolerance may be different for different searchingcriteria.

In one embodiment, patients are identified who may be suitablecandidates for a clinical trial, for which clinical trial exclusion orinclusion criteria were defined. In another embodiment, patients areidentified for a clinical trial model, in order to determine the numberof patients and in order to determine if exclusion or inclusion criteriashould be modified to identify even more potential clinicalinvestigators and study subject populations. In another embodiment, oneor more medical professionals are identified. For example, a potentialmedical expert witness, professor, researcher, etc. may be identified.

In one embodiment, information regarding one or more geographic regionsis also identified in response to a search. In addition to informationthat identifies a potential clinical investigator, study subject, ormedical professional, the individual's detailed location may be also beidentified. The identification of geographic information may be doneautomatically, with or without the user setting up a geographical searchparameter. For example, if the only exclusion or inclusion criteriaspecified by a user involved the age of a patient, a search maynevertheless return information not only identifying patients matchingthe age limitation, but also identifying a general geographical regionwhere those patients live. Such information may be pulled from a claimsdatabase or other data source being searched. As described more below,the geographic area information may be used advantageously to presentsearch results in map-format.

In one embodiment, identification of individuals through searching isdone without revealing any sensitive or protected material. In otherwords, the techniques of this disclosure may be used in a manner thatwould not violate privacy rules or laws (e.g., HIPAA regulations).Searching can take place on data that has been “de-identified” to removereference to, e.g., patient names and social security numbers.Alternatively, searching can take place on original data and thende-identified so that privacy guidelines are met. Techniques known inthe art may be used for the de-identification process.

Additional example situations involving the identification ofinformation through searching, and particularly through searching usingSSTs are provided below. Those having ordinary skill in the art willrecognize that the techniques of this disclosure can be used to identifyinformation for a multitude of other applications, encompassed by theclaims.

Generate Customized Report

In step 110 of FIG. 1, a customized report is generated. The arrangementand format of the customized report may be dictated by the underlyingapplication and desires of the user. In one embodiment, however, one maywish to choose between textual and map-based reports. In FIG. 4C, such achoice is presented to a user through the “wizard” interface discussedpreviously. Here, a user can check for a text report or a map.

A text report may be set up to show the exclusion or inclusion criteriaat issue, the search results themselves in columnar or other convenientformat, and other information (automatically generated or chosen by theuser) that may be pertinent to the analysis. Text reports need not betext-only. A text report can include graphics in the form of pictures,graphs, charts, or the like. A customized report may, if desired, beentirely graphical. Reports may be electronic (e.g., on a computerscreen) and may include video clips, animations, or the like.

FIG. 7 shows example customized reports, which are mixed text andgraphics. In the upper left, a graph shows the number of unique patients(e.g., potential subjects for a clinical trial matching pre-definedexclusion or inclusion criteria) versus age in years. A quick glance atthe graph would reveal to a user that potential subject populations arecentered around an age of approximately 42. At the middle-right of FIG.7 is a graph showing the gender split for the identified patients. Here,over 80% of potential trial subjects are male. At the lower left of FIG.7, geographical information is presented, but not in the form of a map.Unique subjects are charted as a function of their 3-digit zip code,which reveals that New York, N.Y. may be a suitable site for clinicalinvestigator and trial subject recruitment.

A map-based report takes advantage of geographical information pulledfrom the underlying data and may advantageously provide a convenientmechanism for a user to quickly determine what area of the country wouldbe a suitable site for a clinical trial, for a job fair, etc. Withgeographic information accessible, one may study geographic propagationassociated with one or more criteria. For example, map based reports maybe “put in motion” by employing successive map frames. This mayessentially create a “Doppler like” view of the propagation of disease,test subjects, etc. over time. Intensity of the subjects area can beconveyed thematically by color, shading, object size, height, etc. Thetime period can be aggregated by any desired interval to visibly accentseasonal variations or long term trends. FIGS. 8-9 are examples ofmap-based, customized reports. In FIG. 8, a United States map is shown,with an HIV patient distribution as an overlay. HIV patient totals aregiven as a function of region, and information is provided as a functionof region for health providers. FIG. 9 is a zoomed-in region of the mapof FIG. 8.

Those having ordinary skill in the art will recognize that several othertypes of customized reports may be generated. In one embodiment, forexample, one may generate a customized report that is a provider report.The provider report assists in identifying and enrolling clinicalinvestigators and may be similar to those shown in, e.g., FIGS. 8-9.

It may also be beneficial to link data for clinical trial recruitmentapplications with data maintained by, e.g., the Center for DiseaseControl (CDC). This linkage, or other similar linkages, can allow one tocalculate different metrics for confirmation of data or for otherpurposes. For example, by comparing to CDC data, one can determine ifthe number of hits received for a certain condition in a certaingeographical area is “in line” with CDC information for the samecondition. If a search indicates that City A has 5,000 adult patientswith HIV (out of a total of 400,000 adult patients total for City Aresiding in the database(s) being searched), a comparison with CDCinformation regarding HIV rates in City A may serve as a confirmation ofthe 1.25% HIV rate or an alert if the CDC information indicates asubstantially different rate. A confirmation with other data such as CDCdata can be indicated on a customized report through a change in color,a confirmation symbol (e.g., a check mark), or the like.

In one embodiment, information pulled to generate a customized reportmay be linked or compared to information from the U.S. Census to arriveat patient percentage values or the like. For example, if 5,000 adultpatients in City A are identified as being associated with HIVdiagnoses, and one knows that there are 400,000 adult patients total forCity A residing in the database(s) being searched, then one may assumethat about 1.25% of City A's adults have an HIV-related diagnosis. IfCensus data reveals that City A has an adult population of 2.1 millionpeople, one can estimate that there are approximately2,100,000*0.0125=26,250 adults in City A with HIV-related diagnoses.These types of calculations may be used to effectively normalize dataamong cities with vastly different populations—i.e., having 200 “hits”in a large city may actually indicate that it would be a more difficultrecruiting region than a significantly smaller city having the samenumber of hits. Using data that shows city size, one may readily arriveat density or other metrics, which would indicate the number of patientsper square mile, etc. Of course, such techniques are not limited to theidentification of clinical investigators and potential trial subjects.They may be applied to any application discussed here or recognized bythose having ordinary skill in the art.

Turning now to FIG. 2, an example method 200 is shown for handlingstatistical calculations or operations, which can be based onadministrative healthcare claims data or other data. In preferredembodiments, the statistical calculations or operations can be used inconjunction with the techniques illustrated in FIG. 1 and describedherein.

Pre-Generate Factorial Table

The calculation of certain statistics has historically caused problems.With respect to hypergeometric calculations, there has been a tradeoffbetween accuracy and speed. In 1993, Wu published an algorithm thataddressed a number of performance issues involved in processing,especially in dealing with the large factorial sets needed forhypergeometric calculations. However, while performance using the Wualgorithm may be faster than other conventional techniques, theinventors found it insufficient for, e.g., real time return sets fromtens of thousands of attribute/outcome sets requiring full processing.Also, the Wu code requires over/under flow logic when generating aninitial recursion point H(0).

The cumulative hypergeometric function can be calculated in a number ofdifferent ways. Typically each probability “p” value (pdf) is generatedin some fashion and the values are summed to compute the cumulativedensity function (cdf). For large populations, challenges exist in boththe pdf and cdf computations. The pdf calculation can be difficultbecause large factorials must be processed. Wu tackles this issue bybreaking the factorial terms into prime numbers with exponents andreducing each prime/exponent combination to its simplest value. Theremaining primes in the numerator and denominator are then processed insuch a way that over/under flow issues will not manifest. Once thisfirst probability term, h(0), is calculated, then the other probabilityterms can be quickly generated and summed to a cdf using known recursiontechniques; again with care to avoid over/under flow. Issues arise whenmany cdfs need to be computed and the factorial→primesets→cancellation→computation process must be processed, and generateaccurate results, many thousands of times. This process has limitationsbut can be coded directly in SQL (below) to return pdf values. TableALL59701 contains the prime factorization of every factorial 0 through59,701.

SELECT value,row_number( ) over ( ORDER BY value ASC)rank_asc,row_number( ) over ( ORDER BY value DESC) rank_desc FROM (SELECT (POWER(primenumber,((n1_exp+n2_exp)−(d1_exp+d2_exp)))) value FROM( SELECT primenumber ,SUM(CASE WHEN n1=number_of_interest AND prime_expIS NOT NULL THEN prime_exp ELSE 0 END) N1_exp ,SUM(CASE WHENn2=number_of_interest AND prime_exp IS NOT NULL THEN prime_exp ELSE 0END) N2_exp ,SUM(CASE WHEN d1=number_of_interest AND prime_exp IS NOTNULL THEN prime_exp ELSE 0 END) D1_exp ,SUM(CASE WHENd2=number_of_interest AND prime_exp IS NOT NULL THEN prime_exp ELSE 0END) D2_exp FROM ( SELECT a.n1,a.n2,a.d1,a.d2,number_of_interest ,primenumber, (prime_exponent) prime_exp FROM (SELECT 866 n1,1591 n2,1745d1,712 d2 FROM dual) a,ALL59701iot b WHERE a.d1=b.number_of_interest ORa.d2=b.number_of_interest OR a.n1=b.number_of_interest ORa.n2=b.number_of_interest ) GROUP BY primenumber ) WHERE((n1_exp+n2_exp)−(d1_exp+d2_exp)) <>0 ) ORDER BY rank_asc*rank_desc

Table ALL59701 showing the representation of 20000! expressed as aseries of prime^exp values:

NUMBER_OF_INTEREST PRIMENUMBER PRIME_EXPONENT 20,000 2 19,995 20,000 39,996 20,000 5 4,999 20,000 7 3,332 20,000 Many others . . . Many others. . . 20,000 19,991 1 20,000 19,993 1 20,000 19,997 1

A faster method that still yields results accurate to 30 decimal places(as tested against published values, e.g., Wu) uses a different form ofthe factorial table. Instead of expressing factorials as prime^exppairs, the factorial is expressed as a logarithm (any base). In thiscase the computation SQL can be written as:

SELECT SUM (CASE WHEN n1c = number_of_interest THEN ln_factorial ELSE 0END) + SUM (CASE WHEN n2c = number_of_interest THEN ln_factorial ELSE 0END) − SUM (CASE WHEN d1c = number_of_interest THEN ln_factorial ELSE 0END) − SUM (CASE WHEN d2c = number_of_interest THEN ln_factorial ELSE 0END) VALUE INTO h_accum FROM (SELECT /*+ index(b)*/  n1 n1c, n2 n2c, d1d1c, d2 d2c, number_of_interest,  ln_factorial FROM ALL_10M b WHERE d1 =b.number_of_interest  OR d2 = b.number_of_interest  OR n1 =b.number_of_interest  OR n2 = b.number_of_interest) GROUP BY n1c, n2c,d1c, d2c;

Where the table ALL_(—)10M contains the log value of all factorials 0through 10,000,000:

NUMBER_OF_INTEREST LN_FACTORIAL 20000178075.621737198700312867928177311631722

As a check on the internal representation on the values one can see forthis computation of large factorials the value differs from 20001 onlyin the 31st decimal place. Steps may also be taken to representlogarithmic values in the best possible datatype/format for a givenplatform.

SELECT EXP(a.LN_FACTORIAL-B.LN_FACTORIAL)∥″ factorial_check FROM (SELECTa.NUMBER_OF_INTEREST,a.LN_FACTORIAL ln_factorial FROM ALL_10M a WHEREa.NUMBER_OF_INTEREST=20001)a, (SELECTa.NUMBER_OF_INTEREST,a.LN_FACTORIAL ln_factorial FROM ALL_10M a WHEREa.NUMBER_OF_INTEREST=20000) b FACTORIAL_CHECK20000.9999999999999999999999999999999855

In one embodiment, The table ALL_(—)10M may be created and populated inthe following manner

CREATE TABLE ALL_10M ( NUMBER_OF_INTEREST NUMBER NOT NULL, LN_FACTORIALNUMBER NOT NULL, CONSTRAINT PK_ALL_10M PRIMARY KEY (NUMBER_OF_INTEREST)) ORGANIZATION INDEX NOLOGGING; ,and then running a script as below. This may take less than 1 hour on amodest platform to complete.

CREATE OR REPLACE PROCEDURE Factorial10 IS -- fills all_10M table withpairs of: number, LN(number!) i NUMBER; last_i NUMBER; BEGIN INSERT INTOALL_10M VALUES (0, 0); -- 0!=1 and LN(1)=0 last_i := LN (1); FOR i IN 1.. 10000000 LOOP  INSERT INTO ALL_10M VALUES (i, LN (i) + (last_i)); last_i := LN (i) + last_i; IF i/100000=ROUND(i/100000) THEN -- commitevery 100K COMMIT;  dbms_output.put_line( i );  END IF; END LOOP;COMMIT; END Factorial10; /

Hypergeometric calculations have a wide range of uses. One such usepertains to the generation of probability scores for drug safetymeasurements. Consider that two populations are very similar except thatone group has taken drug “A” and the other group has taken drug “B” (ora placebo, or no drug at all). At the initiation of each drug theindex_date is defined. The index date is the beginning of the “outcome”period. Typically, a patient is exposed to a new drug, procedure,diagnosis, event, etc. on the index date. This new condition could be asvaried as “stopped smoking,” “received coronary bypass surgery,” “begantaking drug X,” “was diagnosed with a hernia,” “began working at aparticular location or job,” “began seeing a particular physician,” orany other event, medical or non-medical. The index date for eachindividual in both population set is then normalized to zero. Prior tothe index date each member has a set of attributes including but notlimited to, gender, age, region, diagnosis, procedure, drug codes, etc.After the initiation of the drugs all following codes are tagged asemergent. These emergent conditions may or may not be related to theindex drugs. If the two populations are well matched, one can surmisethat adverse drug effects related to, say, drug A, will be moreprevalent in the drug A population immediately or after prolongedexposure to drug A. Certain subgroups and even “sub-sub” groups may beespecially susceptible to these adverse effects. For instance women maybe more susceptible to, say, dizziness when taking drug “A” than mentaking drug “A”.

Cluster Population Condition Signal Drug A Signal Drug B ALL ALLDizziness weak none Sub group Women Dizziness moderate none Sub-subgroup Women Dizziness strong none on Thyroid drugs

It should be noted that an adverse signal could be actually reflectingthe suppression of an outcome by the other comparing drug. (e.g., ifdrug B happens to cure dizziness in women on thyroid drugs and drug Aneither causes or suppresses dizziness then drug A could show a“dizziness” signal even though the symptom was no higher than the ratein women taking thyroid medication in the general population).

Using such techniques and those of the rest of this disclosure, one maybe capable of, among other things, rapidly:

a. Creating attribute clusters;

b. Creating outcome clusters;

c. Scoring all permutations of attribute/outcome clusters (i.e. findingthe sub groups with the strongest signals for each emergent condition);and

d. Presenting only the most significant permutations.

In one embodiment, a solution to problems associated with prioralgorithms is, in general, two-fold: (1) eliminate the expensive initialfactorial operation associated with hypergeometric calculations by usinga table containing pre-generated factorials (e.g., caching the naturallog (LN) or LOG 10 value (or other differently-based log value) of eachfactorial), and (2) code the recursion for the H(x) integration entirelyin logarithms until a final cumulative P value is computed.

In step 202 of FIG. 2, one or more factorial tables are pre-generated.In a preferred embodiment, these specialized tables allow for thecalculation of hypergeometric statistical results, and they ensure thatsuch calculation proceeds swiftly. The factorial tables may be similarto look-up tables, in which the logarithm of different factorial numbersare listed for quick access. As explained more below, after calculationsor other operations are performed using the logarithms, an exponentialcan be taken.

Search Administrative Healthcare Claims Data

In step 204 of FIG. 2, administrative healthcare claims data issearched. In other embodiments, different types of data may be searchedusing the same techniques. In fact, any searchable repository of data(e.g., database) may benefit from this disclosure, and particularly datafor which hypergeometric statistical results are desired. The actualexecution of the search in step 204 depends upon what statistical resultor analysis the user is seeking to generate. But, in a preferredembodiment, the search step 204 entails the searching of records so thatone subset can be compared against another, as detailed below.

In one embodiment, search step 204 may entail the searching of one ormore SSTs, as detailed above and herein. For example, search step 204may encompass the searching of one or more SSTs alone or in combinationwith the searching of raw data from one or more databases. In thesituation in which SSTs are being searched, the user may see evengreater speed and efficiency.

Comparing One Subset Versus Others

In step 206 of FIG. 2, one subset of administrative healthcare claimsdata is compared against other subsets of the administrative healthcareclaims data in order to arrive at a statistical result (see discussionbelow for embodiments in which the statistical result is based on ahypergeometric calculation). In a preferred embodiment, informationassociated with one patient or physician is compared against informationassociated with all other patients or physicians. The first subset maybe a single record or group of records associated with an individual,while the other subset may be a million or more records associated withother individuals. In other embodiments, the record or recordsassociated with an individual can be compared against the records ofthose who have something in common with the individual—for example, therecords of one physician can be compared against records of otherphysicians practicing in the same medical specialty. Or, the record orrecords of an individual can be compared against the records of otherpatients who suffer from the same or similar medical condition. Thosehaving ordinary skill in the art will recognize that the selection ofdifferent searching subsets can be chosen at will, and according todesire, in accord with the type of statistical result being sought.

Calculating Hypergeometric Statistical Result

In step 208 of FIG. 2, a hypergeometric statistical result is calculatedbased on the comparing step 206, and using the one or more pre-generatedfactorial tables of step 202.

To detect fraudulent coding patterns, discussed more below, one maycompare the medical coding of an individual doctor against the codingpatterns of all other doctors in that specialty, and all the doctors inthe top three specialties that have billed against the same diagnosticcode. The current state of the art hypergeometric method for doing thisanalysis against approximately ten million lives would requireapproximately 100 hours to execute on modern database hardware. As aresult, this sort of comprehensive fraud detection would be limited tospecific physicians that are suspect of fraudulent behavior.

Using pre-generated factorial tables and the other innovations of thisdisclosure, however, (1) eliminates the expensive initial factorialoperation by using a table containing pre-generated factorials (e.g.,caching the LOG 10 value of each factorial) and, (2) coding therecursion for the hypergeometric values entirely in logarithms until afinal cumulative value is computed. This process reduces the calculationtime for the same fraud screen against approximation ten million livesto approximately six minutes (a speed increase of about 1000 times). Asa result, a fraud screen can be continuously run against a much largernumber of doctors, and new hypothesis for fraud detection can bedeveloped much more quickly (e.g., six minutes to see if there is auseful signal, instead of 100 hours). With this type of speed, one canapply different “what if” tests to extremely large data sets and getanswers in minutes and hours, not weeks and months.

Computationally expensive processes in the hypergeometric calculationshave been recoded by the inventors using factorial lookups and naturallogarithms to yield accurate P values in milliseconds. As specified byWu (shown here in the two equation sets immediately below) and others, aspecific pdf can be obtained by direct calculation of factorials. Below,N is the total population, n is a subgroup of the population, r is thesample set taken form the population without replacement, and x is thenumber of “hits” in the sample set.

${{h( {{x;r},n,N} )} = \frac{{n!}{r!}{( {N - n} )!}{( {N - r} )!}}{{N!}{x!}{( {n - x} )!}{( {r - x} )!}{( {N - n - r + x} )!}}},$

where max(0,r−N+n)≦x≦min(r,n).

Or, for the simple case when x=0

$\begin{matrix}{{h( {{0;r},n,N} )} = \frac{{m( {m - 1} )}{\ldots( {m - r + 1} )}}{{N( {N - 1} )}{\ldots( {N - r + 1} )}}} \\{{= \frac{{m!}{( {N - r} )!}}{{N!}{( {m - r} )!}}},}\end{matrix}$ where  m = N − n.

Prior to recursion, if the starting pdf value (initial recursion point)is not zero because, e.g.:

-   -   1. Summing the smaller tail would be faster or    -   2. Zero is an undefined starting point,        then, for these special cases, one may use the longer form:

 SELECT SUM (CASE WHEN n1c = number_of_interest  THEN ln_factorial ELSE0  END)  + SUM (CASE WHEN n2c = number_of_interest  THEN ln_factorialELSE 0  END) + SUM (CASE WHEN n3c = number_of_interest  THENln_factorial ELSE 0  END) + SUM (CASE WHEN n4c = number_of_interest THEN ln_factorial ELSE 0  END)  − SUM (CASE WHEN d1c =number_of_interest  THEN ln_factorial ELSE 0  END)  − SUM (CASE WHEN d2c= number_of_interest  THEN ln_factorial ELSE 0  END) − SUM (CASE WHENd3c = number_of_interest  THEN ln_factorial ELSE 0  END) − SUM (CASEWHEN d4c = number_of_interest  THEN ln_factorial ELSE 0  END) − SUM(CASE WHEN d5c = number_of_interest  THEN ln_factorial ELSE 0  END)VALUE INTO h_accum FROM (SELECT /*+ index(b)*/   n1 n1c, n2 n2c, n3 n3c,n4 n4c, d1 d1c, d2 d2c,d3 d3c, d4   d4c,d5 d5c, number_of_interest,  ln_factorial  FROM ALL_10M B WHERE d1 = B.number_of_interest  OR d2 =B.number_of_interest OR d3 = B.number_of_interest OR d4 =B.number_of_interest OR d5 = B.number_of_interest  OR n1 =B.number_of_interest  OR n2 = B.number_of_interest OR n3 =B.number_of_interest  OR n4 = B.number_of_interest); h_sum := EXP(h_accum); h_all := h_accum; tail_integration_pt:=1 for typical h(0)starts;  or tail_integration_pt:=(n−(ntot−r))+1 <for starting at firstdefined  point>;

Once the ln(pdf) is processed, shown here as h_accum, it can be directlyconverted via exp(h_accum), shown as h_sum. This pdf can then be used torecursively generate the additional terms in one of two ways. If onewishes to avoid over/under flow issues at the expense of a modestperformance hit, the recursion steps can be processed in logarithms.

FOR i IN tail_integration_pt .. k LOOP x := i − 1; h_all := h_all + LN ((n − x) * (r − x) / (x + 1) / (ntot − n − r + x + 1)); h_sum := h_sum +EXP (h_all); END LOOP; RETURN h_sum;

Alternately, one can recursively process in natural numbers after theexp conversion taking care to avoid over/underflow.

FOR i IN tail_integration_pt .. k LOOP x := i − 1; h_all := h_all * ( (n− x) * (r − x) / (x + 1) / (ntot − n − r + x + 1)); h_sum := h_sum +(h_all); END LOOP; RETURN h_sum;

Yet another possibility is to repeatedly call the master SQL and “exp”and then sum each h(x) term one by one.

Hypergeometric processing has applications in quality control, patternrecognition, cluster validity analysis, tree classifier design, imagetemplate matching and other areas.

Techniques of this disclosure allow one to apply statistical methodologyto large data sets or dynamic data feeds for real-time consideration ofdata population statistical characteristics. Table structures may beoptimized such that blocks (e.g., ORACLE blocks) are “rich” in thedesired filter criteria. This enables access of hundreds of thousands oflines of data in a few seconds. One can thus quickly find data ofinterest, cluster it into appropriate groupings, apply hypergeometricprocessing to the data sets, and present only the “interesting” cases,as determined by their hypergeometric score, to a user or other.

Detect Fraud, Rate Physicians, and Other Applications

In step 210 of FIG. 2, one uses the hypergeometric statistical result todraw some conclusion from the administrative healthcare claims data. Inone embodiment, the steps of FIG. 2 may be used to determine whether oneor more physicians may be involved with medical fraud. In such anembodiment, the search and comparison steps 204 and 206 may involvecomparing billing practices (e.g., CPT or other medical codes)associated with the physician against the billing practices of othergroups of physicians (e.g., against other groups in total, againstgroups having the same medical specialty, against groups sharing thesame geographic region, against groups of similar age, etc.). In oneembodiment, results of the fraud investigation can be output to form acustomized report.

In the medical field, fraud detection is an area of interest.Opportunistic behavior by medical staff can manifest in unlikelyproportions of lucrative procedures being performed. Rather than merelystating Dr. X performs procedure Y a certain percentage (e.g., 30%) moreoften than his or her peers, one can compute based on the totalpopulation of patients requiring procedure Y, the likelihood that Dr. Xwould have a certain number of patients (e.g., 30) requiring theprocedure. If the likelihood is greater than a certain amount (e.g., 1in 10,000,000 or other value defined by a user or other entity), actioncan be taken to look more closely at the case. This can be extended toany pairings of medical codes. For example, this may be extended toscenarios such as:

-   -   hospital A dispensed a narcotic for diagnosis B seventy times        for its 120 patients with diagnosis B. Nationally, 270 patients        were treated in hospitals this way out of 3400 with diagnosis B.        How likely is this scenario?        This is one example only with example figures. Those of ordinary        skill in the art will recognize other applications using these        same or equivalent techniques. Many factors may need to be taken        into account before action would be warranted with respect to        fraud enforcement. Techniques of this disclosure, however, can        quickly generate a working list sorted by least to most likely        events to assist in the fraud detection/enforcement process. For        disease clustering, one can call the hypergeometric to calculate        the likelihood that X cases of disease Y would occur in a        particular geographic region in a given interval knowing Z cases        occurred nationally in that interval. Again, various other        applications will be apparent to those having ordinary skill in        the art.

An example customized report may constitute a graph of utilizationpercentage versus medical billing code for a first physician versus agroup of other physicians (e.g., others in his or her specialty). Anexample graph is shown in FIG. 10. There, the red bar (left) representsa physician under investigation. The blue bar (right) representsthousands of other physicians practicing in the same specialty. The datareveals that the physician under investigation utilizes billing code Xsignificantly more than his or her peers. This may signify fraud.

Comparing one subset of individuals versus another and searching forresulting patterns, especially in conjunction with hypergeometriccalculations, can be applied to many other applications other thanmedical fraud detection. In one embodiment, one may use the comparisonsand other steps like those in FIG. 2 to rate or score physicians. Anycomparison in view of a peer group can constitute a score. For physicianquality scoring, one might measure patient wellness (according to one ormore of many methods known in the art) across various ETG groupings anddetermine a physician's quality of care measure against peers treatingindividuals in similar ETG groups. This could be extended to look forunconventional treatment patterns that yield beneficial results eventhough they may not conform to existing “best practices”. This may ormay not be a drug related effect, or may involve combination of drugsand procedures. For example, one may generate statistics such as: of theW people with chronic condition “A” who had a typically unrelatedprocedure “B”, X showed improvement as measured by lab results OC1,hospital stay length OC2, or other wellness indication. Of the totalpopulation of Y people with chronic condition “A” who had similartreatments except for procedure “B”, Z showed improvement. Suchstatistics may point to beneficial results, correlations, may suggestresearch avenues, etc.

In another embodiment, the comparisons may be used for marketing. Onemay run comparisons of physicians based on geographical area todetermine if there are any patterns concerning drugs being prescribed.If it is found that one region lags behind, marketers may want to focuson that region to bring its statistics in line with other regions. Onemay run comparisons of physicians based on where they went to medicalschool to determine if there is a correlation between medical school anddrugs being prescribed. Marketers may then want to focus efforts on someschools more than others. Those of ordinary skill in the art, with thebenefit of this disclosure, will recognize many other similar marketingapplications.

In another embodiment, the comparisons may be used for drug safetystudies. The i3 APERIO DRUG REGISTRY product from Ingenix, Inc. can beused for such purposes. By looking at the relevant intersection pointsof thousands, millions, or billions of data elements, one may answer thequestion, “Is drug A safer to use than drug B?.” Specifically, the useof a real-time hypergeometric calculation allows one to place billionsof data points into their respective numerator/denominator positions andto identify the most meaningful data intersections needed to answer thequestion. It is believed that this will open up new signal detectionopportunities in at least the drug safety and fraud detection arenas.Combining the innovations with hypergeometric calculations describedhere with the SST innovations, one may quickly integrate hypergeometricfunctions with extremely large data sets and scale to even larger datasets, while minimally reducing speed.

SSTs Used in Combination with Hypergeometric Factorial Tables

In different embodiments, both SSTs and factorial tables havinglogarithmic entries for improved hypergeometric calculations can be usedtogether to provide even more robust data mining and analysis. Forexample, the hypergeometric may be used to find disease hot spots andphysician hot spots. Additionally, both SSTs and factorial tables may beused in conjunction with the identification of potential subjects forclinical trials. Using both SSTs and factorial tables, clinical researchorganizations are given the ability to pinpoint a population substrateand identify clinical investigators for drug trials to answer questionssuch as, “What is the context of the disease in the U.S. and which sitesshould be selected for conduct of the study based on diseaseprevalence?”

Both SSTs and factorial tables can also be applied to applications inwhich a medical expert witness (or other professional) is beingrecruited. For example, attorneys and law firms are provided the abilityto answer questions such as, “Who can provide medical expert testimonyin a case involving medical conditions A, B and C?”

Use of Technology for Modeling

Because one can so quickly mine and analyze massive amounts ofadministrative healthcare claims data, the techniques of this disclosureallow users to model clinical trials or other application in real-timeor near-real-time. For example, individual iterations of modeling mayoccur approximately once per 10 minutes in one embodiment, once per 5minutes in another embodiment, once per 2 minutes in another embodiment,once per 1 minute in another embodiment, once per 30 seconds in anotherembodiment, once per 10 seconds in another embodiment, once per 5seconds in another embodiment, once per 2 seconds in another embodiment,once per second in another embodiment, once per ½ second in anotherembodiment, once per ¼second in another embodiment, and so on. Usingclinical trials as an example, one may define exclusion or inclusioncriteria, run a search, determine a potential subject pool, and modelhow that population pool would change by modifying the exclusion orinclusion criteria. In a preferred embodiment, the user compares thepotential subject pool returned by a search against a minimum subjectparticipation—a quantitative measure (e.g., total subjects, subjectdensity, etc.). If the potential subject pool is less than the minimumsubject participation, the user may modify the exclusion or inclusioncriteria until the minimum subject participation is met or exceeded.

In one embodiment, the exclusion or inclusion criteria may be modifiedautomatically until a target subject participation value is at least metor exceeded (or, in other embodiments, until the potential patient poolis less than a given target). The automatic modifications to theexclusion or inclusion criteria may be done within pre-defined rangesset up by the user, according to different priorities assigned by theuser, or through other means to ensure that the modified exclusion orinclusion criteria still define a useful criteria for the study. Neuralnetwork technology or other computer science principles known in the artmay be employed in this modeling process. In embodiments using automaticmodifications, the modifications may be done iteratively until a targetis met. If the exclusion or inclusion criteria do not meet the targetafter a pre-determined number of iterations or time period an error oralert may be generated.

Use of Technology for Additional Applications

The numbered list below summarizes some applications already discussedhere and includes others that may be readily adapted using thedescription above. The applications listed below may make use of, e.g.,SSTs and/or factorial tables for searching and statistical calculations,respectively. The applications may track the steps in FIG. 1 or 2. Forexample, following FIG. 1, searching criteria may be defined, followedby pre-generation of SSTs, followed by a search, followed by theidentification of pertinent “hits,” followed by the generation of acustomized report presenting or summarizing information about the “hits”in a user-convenient format, such as by geography. Likewise forstatistical calculations, and following FIG. 2, the applications listedbelow may pre-generate one or more factorial tables, search data,compare one subset of data versus others, calculate a statistic usingthe factorial tables, and then reach (or assist the user in reaching orpostulating) a conclusion based on the statistic that was calculated.

Those having ordinary skill in the art will recognize that there aremany other applications, and those mentioned here are not meant to be anexhaustive list.

1. Clinical trial investigator and potential trial subjectidentification. Researchers are able to identify potential clinicalinvestigators and subject pools quickly. Geographical or other types ofreports may be generated. Due to speed, clinical trials can be modeledto arrive at exclusion or inclusion criteria suitable for enrollinginvestigators and recruiting an adequate number of subjects in advanceof first participant's, first visit. This application can save millionsof dollars by avoiding delay during the recruitment phase of a trial.

2. Recruitment of medical directors and expert medical witnesses.Lawyers, legal assistants, hospital and physician recruiting firms, orother users are able to quickly determine where a suitable medicaldirectors and medical expert witnesses may be found or, more directly,who might be a good fit for a particular position or case. Searchingparameters may be chosen to ensure that the expert will have theappropriate experience or attributes being sought by the recruiting firmor legal team. Due to speed, modeling may be done—the user can model thenumber or type of experts depending on changes in searching parameters.

3. Analysis of medical litigation. Through the improved data mining andanalysis techniques described here (e.g., use of SSTs and/or factorialtables), lawyers or other users may assess the validity or likelihood ofsuccess of a medical litigation. For example, one may analyze a historicclaims profile to identify treatment profiles of subjects similar to aclient to determine, e.g., other maladies suffered by such individuals.By comparing the client's treatments against hundreds, thousands, ormillions of similarly-situated treatment patterns, the legal team maydiscover “holes” in their case or opportunities for additionalarguments/theories.

4. Counseling of medical school students. By mining and analyzing claimsdata, one may present to medical school students (or others) an overviewof the type of diseases that, on average, are being seen by specificspecialties. Such information may be useful to various specialtycolleges for recruitment.

5. Ancillary Product Marketing. Through analysis of claims data or otherdata, one may correlate, e.g., use of a particular drug with otherbuying habits (e.g., if a person uses drug A, it appears he or she mayalso use drug/product X, Y, or Z). This information may be used inmarketing. For example, marketers may use the correlations for onlineadvertising—along with links related to drug A, it may be useful todisplay banner ads for drug/product X, Y, or Z.

6. Job Placement. One may find suitable job candidates for a variety ofdifferent jobs using the techniques described here. For example,pharmaceutical companies may search for a candidate having particularexperience as a physician prescribing a certain category of drugs, orinvestigating certain illnesses.

7. Continuing Medical Education (CME). Techniques of this disclosure mayallow CME companies the ability to “measure” practice patterns beforeand after a CME program.

8. Marketing. Techniques of this disclosure may allow marketers theability to “measure” how effective an ad campaign was. For example, ifmillions of dollars were spent in city X promoting drug Y, one couldmonitor over a period of time whether the prescribing habits ofphysicians changed with respect to drug Y in city X. One can monitor forchanges in physician treatments, drug penetration, sales volume, growthtrends, etc.

9. Regulatory application. Regulatory agencies charged with theresponsibility of clinical trial oversight (e.g., FDA in the US, EMEA inthe European Union) would be able to modify requirements forregistrational trials based on feasibility evidence as well as evaluatehow compliant to marketing approval particular drugs are with reportsgenerated from techniques of this disclosure. Agencies such as the CDCor WHO would be able to implement real time surveillance programs ofdrug resistance and emerging pathogens from techniques of thisdisclosure.

10. Physician Scorecard. By comparing one physician versus others invirtually any category supported by underlying data, a scorecard systemcan be created. This, for example, may provide a comparative analysisfor each physician of coding practices relative to a chosen benchmark(could be billing, outcomes, script usage, etc.). An “alerting” systemmay be included to trigger an alarm if threshold values are exceeded.

11. PharmaSolutions. Through quick analysis of claims data, one maycharacterize key issues for a drug prior and after market launch—(1)compliance with regimen compared to others in-class meds, (2) usage ofdrugs by indication that would provide insight into marketing needs, (3)prescribing habits of particular drugs or drug classes based onphysician profiling (e.g., demographics or training institution)]., or(4) evaluation of drug utilization based on demographics or otheravailable claims variables for market characterization.

12. Consumer Preventive Health Solutions. Through analysis of claimsdata as taught here, a health consumer could effectively “see” his orher future. For example, a person could enter his or her currentdemographic and disease characteristics and then “see” 5, 10, 15, and 20years into the future by looking at similar consumers in that age groupand look at what types of claims are being captured. Conversely, ahealth system may want to know what a particular consumer may face interms of claims in the future. Decisions about such consumers asenrollees can then be made.

13. PhysicianClustering. In another marketing-related, application, onemay cluster physicians by age, demographics, place of training anddetermine if there are marketing holes in their prescribing patterns.Or, one may be alerted to poor training that is responsible forillogical prescribing patterns.

14. HealthConsumer. Through analysis of claims data, one may find the“best fit” physician for a particular patient. Health consumers would beable to evaluate, in real time, which physician best met their need withrespect to (1) geographic location, (2) mix of patient population, (3)quality measure, and (4) outcomes for patient with similar diseaseprofiles.

15. Serendipitous Reporting System (SRS): One may use hypergeometricsand SSTs to compare claims of subjects and retrospectively determinewhich interventions (procedure or drug) may have had a positiveinfluence on their disease.

16. Disease Surveillance System Network (DSSN): One may usehypergeometrics and SSTs to identify in real time potential outbreaks ofdisease that would be considered statistically unlikely to be in accordwith background rates.

Hardware

Turning now to FIG. 3, a schematic diagram of a computer system 300,including computer 302 is shown. Computer readable medium 304, which ishere shown as a disk as an example, can be used to house softwaresuitable for carrying out certain techniques of this disclosure. Thesoftware may be written using any of a number of programming languages.Suitable languages include, but are not limited to, BASIC, FORTRAN,PASCAL, C, C++, C#, JAVA, HTML, XML, PERL, SQL, db internal languages(e.g., PL/SQL), etc. In one embodiment, commercially available softwarerunning one or more scripts or routines can be used for carrying out theinvention. For example, one may use MICROSOFT EXCEL or anotherspreadsheet with appropriate database and analysis functionality tocarry out the invention. One may use MATLAB or other mathematicalpackages for carrying out the invention as well. One may use commercialdatabase software, scripting routines, and the like.

Computer readable medium 304 may be any medium available and which issuitable for storage and which allows for the eventual execution of codeby a computing device. Code may be housed on a computer file, a softwarepackage, a hard drive, a FLASH device, a floppy disk, a tape, a CD-ROM,a DVD, a network drive, a hole-punched card, an instrument, an ASIC,firmware, a “plug-in” for other software, web-based applications, RAM,ROM, etc.

Computer 302 may be any computing device including but not limited to apersonal computer (e.g., a desktop, laptop, tablet, pen, or othercomputer operated by a user), a personal digital assistant (PDA), orother devices.

In some embodiments, the computer-readable media 304 and computer 302may be networked. One may use a terminal device running software from aremote server, wired or wirelessly. Input from a user or other coupledsystem components may be gathered through one or more known techniquessuch as a keyboard or mouse. Output, if desired, may be achieved throughone or more known techniques such as an output file, printer, facsimile,e-mail, web-posting, or the like. Storage may be achieved internally orexternally. Any integral or remote display type may be used includingbut not limited to a cathode ray tube (CRT) or liquid crystal display(LCD). One or more display panels may also constitute a display. Inother embodiments, a traditional display may not be required, and thecomputer-readable media may operate through appropriate voice and/or keycommands.

The following examples are included to demonstrate specific embodimentsof this disclosure. It should be appreciated by those of ordinary skillin the art that the techniques disclosed in the examples that followrepresent techniques discovered by the inventors to function well in thepractice of the invention, and thus can be considered to constitutespecific modes for its practice. However, those of ordinary skill in theart should, in light of the present disclosure, appreciate that manychanges can be made in the specific embodiments which are disclosed andstill obtain a like or similar result without departing from the spiritand scope of the invention.

Example 1

The code below is directed to one example embodiment and includesbackground information on the pre-generated tables used in the datamining algorithm.

Pre-Generated Tables with Drug Pair, Version Partitions

The following four tables are pre-populated and partitioned by drug pairand version.

CLUSTER_BASELINE_POPS

-   -   This table contains all possible attributes for each study        participant.

CREATE TABLE CLUSTER_BASELINE_POPS (  PRODUCT_ID  NUMBER  NOT NULL, VERSION_ID NUMBER NOT NULL,  NEWINDV_ID  VARCHAR2(17 BYTE) NOT NULL, GROUPFLAG  NUMBER(1)  NOT NULL,  ATTRIBUTE VARCHAR2(30 BYTE), ATTRIBUTE_VALUE VARCHAR2(64 BYTE) )

Where product_id, and version_id are partitioning information

Newindv_id is a unique patient identifier

Groupfalg indicates the population set

Attribute and attribute value generalize all possible attributes, e.g.:newindiv_id 1723423_(—)2 might have:

Attribute Attribute Value Gender Male Dx_code  410 Region South Px_code87010 Age   27

CLUSTER_XD_XC_PREJOIN_DX_OP

This table contains all possible attribute/outcome pairings for Dxoutcome-outpatient events for each study participant.

CREATE TABLE CLUSTER_XD_XC_PREJOIN_DX_OP (  PRODUCT_ID  NUMBER  NOTNULL,  VERSION_ID NUMBER NOT NULL,  NEWINDV_ID  VARCHAR2(17 BYTE) NOTNULL,  GROUPFLAG  NUMBER(1)  NOT NULL,  ATTRIBUTE VARCHAR2(30 BYTE), ATTRIBUTE_VALUE VARCHAR2(64 BYTE),  OUTCOME_CLASS VARCHAR2(22 BYTE), OUTCOME_TYPE VARCHAR2(4 BYTE),  DAYS_IN_STUDY NUMBER )

Where outcome_class and outcome_type are generic fields for any outcomeand days_in_study indicates when the outcome occurred as measured fromthe index_date, e.g.:

Outcome_class Outcome_type days_in_study DX_OUTPATIENT 337 103,where outcome type 337 might be “Disorders of the autonomic nervoussystem,” or other information.

CLUSTER_XD_XC_PREJOIN_DX_IP

This table contains all possible attribute/outcome pairings for Dxoutcome-inpatient events for each study participant.

CLUSTER_XD_XC_PREJOIN_RX

This table contains all possible attribute/outcome pairings for Rxoutcome events for each study participant.

The CLUSTER_XD_CD_PREJOIN_{value} tables, such as the two mentionedabove, may be of the following example form:

CREATE TABLE CLUSTER_XD_XC_PREJOIN_???? (  PRODUCT_ID  NUMBER  NOT NULL, VERSION_ID NUMBER NOT NULL,  NEWINDV_ID  VARCHAR2(17 BYTE) NOT NULL, GROUPFLAG  NUMBER(1)  NOT NULL,  ATTRIBUTE VARCHAR2(30 BYTE), ATTRIBUTE_VALUE VARCHAR2(64 BYTE),  OUTCOME_CLASS VARCHAR2(22 BYTE), OUTCOME_TYPE VARCHAR2(4 BYTE),  DAYS_IN_STUDY NUMBER ) TABLESPACE USERSPCTUSED 0 PCTFREE 10 INITRANS 1 MAXTRANS 255 PARTITION BY RANGE(PRODUCT_ID, VERSION_ID) ( partition terms)

Pre-generated tables not related to drug pairs

Additionally a couple other pre-generated tables are used.

ALL_CODE_XWALK Contains Dx/Px/Rx code descriptions CREATE TABLEALL_CODE_XWALK ( CODE_TYPE CHAR(2 BYTE), CODE_SET VARCHAR2(8 BYTE),CODE_DESC VARCHAR2(220 BYTE) )

Where code_type is PX,RX or DX, Code_set is the actual code, andcode_desc is the description. Note this can be extended to any attributerequiring a description term.

ALL_(—) 10M

This table contains the natural log of the factorial of each number from0 through 10,000,000. This table is called by wu4_function9biot toprovide pre-generated factorials for computation of the first P-valuefrom which to integrate (usually zero but accommodates highly skewedsets where zero is not a viable starting pint).

CREATE OR REPLACE PROCEDURE Factorial10 IS -- fills all_10M table withpairs of: number, LN(number!)  i NUMBER;  last_i NUMBER; BEGIN INSERTINTO ALL_10M VALUES (0, 0); -- 0!=1, ln(1)=0 last_i := LN (1); FOR i IN1 .. 10000000 LOOP  INSERT INTO ALL_10M VALUES (i, LN (i) + (last_i)); last_i := LN (i) + last_i; IF i/100000=ROUND(i/100000) THEN COMMIT; dbms_output.put_line( i );  END IF; END LOOP; COMMIT; END Factorial10;/ =========================== CREATE TABLE ALL_10M (  NUMBER_OF_INTERESTNUMBER NOT NULL,  LN_FACTORIAL NUMBER NOT NULL,  CONSTRAINT PK_ALL_10MPRIMARY KEY (NUMBER_OF_INTEREST) ) ORGANIZATION INDEX NOLOGGING;

Clustered Outcomes Processing Example

A client is interested in the outcomes associated with the population ofpatients taking thyroid hormones prior to their initiation date on theKetek/Biaxin drug pair. The user creates the filter “IN Thyroidhormones” and applies it to “Dx OUT” in the data mining section for theKetek/Biaxin drug pair.

Process will then commence in multiple stages (in order of operation):

Identify, aggregate, join and filter attribute/outcome pairs

1. Identify the population “in play” for the analysis, e.g., thosetaking thyroid hormones in the baseline period.

2. Collect all attributes belonging to these individuals in theappropriate pre-joined CLUSTER_BASELINE_POPS partition

3. Aggregate this set counting unique patients in each attribute

a. We'll call this Set “A” (Nd and Nc counts for each attribute)

Set “A”—steps 1, 2, 3, 3a:

SELECT attribute,attribute_value ,COUNT (UNIQUE CASE WHEN groupflag=1THEN newindv_id ELSE NULL END) nd ,COUNT (UNIQUE CASE WHEN groupflag=0THEN newindv_id ELSE NULL END) nc FROM CLUSTER_BASELINE_POPS WHEREproduct_id=2 AND version id=200503 -- AND newindv_id IN ( selectdistinct newindv_id from rx_baseline where drug_desc like ‘%thyroidhormones%’) - could be drug class, NDC codes, etc. GROUP BYattribute,attribute_value;

4. Create a placeholder attribute called “Baseline” and collect allattributes belonging to these individuals in the appropriate pre-joinedCLUSTER_(—) BASELINE_POPS partition (using GENDER to prune)

5. Aggregate this set counting unique patients in the “Baseline”

a. We'll call this Set “B” (Nd and Nc counts for “Baseline”, i.e. filteronly attribute)

Set “B”—steps 4, 5, 5a:

SELECT attribute,attribute_value,SUM(nd) nd,SUM(nc) nc FROM ( SELECT‘Baseline’ attribute,‘ ’ attribute_value ,COUNT (UNIQUE CASE WHENgroupflag=1 THEN newindv_id ELSE NULL END) nd ,COUNT (UNIQUE CASE WHENgroupflag=0 THEN newindv_id ELSE NULL END) nc FROM CLUSTER_BASELINE_POPSWHERE product_id=2 AND version_id=200503 AND attribute=‘GENDER’ ANDnewindv_id IN ( select distinct newindv_id from rx_baseline wheredrug_desc like ‘%thyroid hormones%’) - could be drug class, NDC codes,etc. GROUP BY attribute,attribute_value

6. Merge Sets “A” and “B” into Set “AB” (all Nd and Nc counts)

UNION the two sets together and tag the combined set nd_nc. We have nowgenerated the counts for every possible attribute.

7. Collect all permutation sets belonging to these individuals in theappropriate pre-joined CLUSTER_XD_XC_PREJOIN_DX_OP partition. Now let'scollect the outcome sets and count the outcome population for eachattribute/outcome pair.

8. Aggregate this set counting unique patients in each attribute/outcomepairing

a. This becomes Cluster Set “C” (Xd and Xc counts for eachattribute/outcome pair)

Set “C” SELECT attribute,attribute_value,outcome_class,outcome_type,COUNT (UNIQUE CASE WHEN groupflag=1 THEN newindv_id ELSE NULL END) xd,COUNT (UNIQUE CASE WHEN groupflag=0 THEN newindv_id ELSE NULL END) xcFROM CLUSTER_XD_XC_PREJOIN_DX_OP -- one of three outcome tables WHEREproduct_id=2 AND version_id=200503 AND newindv_id IN ( select distinctnewindv_id from rx_baseline where drug_desc like ‘%thyroid hormones%’) -could be drug class, NDC codes, etc. GROUP BYattribute,attribute_value,outcome_class,outcome_type

9. Create a placeholder attribute called “Baseline” and collect alloutcome permutation sets belonging to these individuals in theappropriate pre-joined CLUSTER_XD_XC_PREJOIN_DX_OP partition (usingGENDER to prune). Again using GENDER to ensure a look at the entirebaseline population we'll count the outcome population for eachbaseline/outcome pair.

10. Aggregate this set counting unique patients in each“Baseline”/outcome pairing

a. This becomes Cluster Set “D” (Xd and Xc counts for each“Baseline”/outcome pair)

Set “D” SELECT attribute,attribute_value,outcome_class,outcome_type,SUM(xd) xd,SUM(xc) xc FROM (SELECT ′Baseline′ attribute,′ ′attribute_value,outcome_class,outcome_type ,COUNT (UNIQUE CASE WHENgroupflag=1 THEN newindv_id ELSE NULL END) xd ,COUNT (UNIQUE CASE WHENgroupflag=0 THEN newindv_id ELSE NULL END) xc FROMCLUSTER_XD_XC_PREJOIN_DX_OP -- one of three outcome tables WHEREproduct_id=2 AND version_id=200503 AND attribute=′GENDER′ -- Attachfilter here AND newindv_id IN ( select distinct newindv_id fromrx_baseline where drug_desc like ‘%thyroid hormones%’) - could be drugclass, NDC codes, etc. GROUP BYattribute,attribute_value,outcome_class,outcome_type ) GROUP BYattribute, attribute_value,outcome_class,outcome_type

11. Merge Sets “C” and “D” into Set “CD” (all Xd and Xc count).

Merge the two sets using a UNION and tag as xd_cd

12. Join Set “AB” to Set “CD” by attribute becoming Set “ABCD” WhereSets “A” and “C” form the clustered outcomes while sets “B” and “D”comprise the dynamic baseline.

13. Discard trivial cases keeping only rows where nd>3 AND nc>3 AND(xd+xc)>3 AND ((nc+nd)−(xc+xd))>3

Using the code below, one can join the two sets (into “ABCD”) andeliminate trivial cases (this is optional if one wants to keep andprocess such cases).

WHERE nd_nc.attribute=xd_xc.attribute AND

nd_nc.attribute_value=xd_xc.attribute_value AND nd>3 AND nc>3 AND

(xd+xc)>3 AND ((nc+nd)−(xc+xd))>3

14. Process RR and Yates CI for Set “ABCD”.

15. Suppress processing of uninteresting cases, processing only thosewhere POWER(ABS(xd−(xd+xc)*nd/(nd+nc))−0.5,2)>=(4*(xd+xc)*nd/(nd+nc)*(1−nd/(nd+nc)))(this is not done at step 13 due to an Oracle nuance)

Begin Cumulative Hypergeometric computation (AKA cdf)

16. Pass Xprime, Nprime, Xtot, Ntot rows from Set “ABCD” into the Oraclefunction wu4_function9biot

The code below works with the population sets in the attribute outcomepairs generated in steps 1 to 13. Drug and comparator values are swappedprior to hypergeometric computation if the comparator has the strongeroutcome signal. The log 10 value of these signals are demarked with aflipped score_sign. Prior to the hypergeometric calculationWu4_funtion9biot( ) a case statement performs a secondary filter onuninteresting cases (those that will not have a significant score). Thiscase statement can be removed if all cases are desired to be processed.The Relative Risk and Yates confidence interval are also processed inthe code as supplementary information on the population set. The scoresfrom the hypergeometric are converted by log 10 for readability but canbe show in their original form if desired.

d.attribute,d.attribute_value,a.code_desc ″Attribute valuedesc″,d.outcome_class,d.outcome_type,o.code_desc ″Outcome typedesc″,d.xd,d.nd,d.xc,d.nc, CASE WHEN d.xc>0 THEN TO_CHAR(d.xd*d.nc/(d.xc*d.nd),′9999990.9′) ELSE ′--′ END rr, CASE WHEN(x−plower*x)>0 THEN (plower*x/d.nd)/((x−plower*x)/d.nc) ELSE 0 ENDrrlower, CASE WHEN (x−pupper*x)>0 THEN(pupper*x/d.nd)/((x−pupper*x)/d.nc) ELSE 0 END rrupper, CASE WHENcumul_hyper>1e−128 THEN score_sign*TRUNC(LOG(10,d.cumul_hyper)) ELSE−10*score_sign END score FROM ( SELECTc.*,(−b_high+SQRT(POWER(b_high,2)−4*a*c_high))/(2*a)pupper,(−b_low-SQRT(POWER(b_low,2)−4*a*c_low))/(2*a) plower, CASE WHENPOWER(ABS(xd−(xd+xc)*nd/(nd+nc))−.5,2) >=(8*(xd+xc)*nd/(nd+nc)*(1−nd/(nd+nc))) THENaperio.Wu4_Function9biot(x_prime,n_prime,xd+xc,nd+nc) ELSE 1 ENDcumul_hyper FROM ( SELECT b.*,1+d a,−2*pd_low-d b_low,−2*pd_high-db_high,POWER(pd_low,2) c_low,POWER(pd_high,2) c_high FROM ( SELECTa.*,xd+xc x,POWER(1.96,2)/(xd+xc) d,(Xd−.5)/(xd+xc)pd_low,(Xd+.5)/(xd+xc) pd_high FROM ( SELECTnd_nc.attribute,nd_nc.attribute_value,xd_xc.outcome_class,xd_xc.outcome_(—)type,xd_xc.xd,nd_nc.nd,xd_xc.xc,nd_nc.nc ,CASE WHENxd>(xd+xc)*nd/(nd+nc) THEN xc ELSE xd END x_prime ,CASE WHENxd>(xd+xc)*nd/(nd+nc) THEN nc ELSE nd END n_prime ,CASE WHENxd>(xd+xc)*nd/(nd+nc) THEN −1 ELSE CASE WHEN xd= (xd+xc)*nd/(nd+nc) THEN0 ELSE 1 END END score_sign FROM Set “ABCD”

17. Wu4_function9biot will determine if the tail starting point is zeroor some other value (for sets where zero is not valid)

a. For a zero tail start the natural log of the hypergeometric iscomputed directly in the A11_(—)10M SQL call per the Wu (2.1) equation

b. For a non-zero tail start the natural log of the hypergeometric iscomputed directly in the A11_(—)10M SQL call per the Wu (1.8) equation

18. These tail starting points are then recursively computed and summedup to the natural log Xprime value per the Wu (1.2) equation. Theln(cdf) is then converted to cdf using EXP( ) and truncated to aninteger value.

Output Filtering for Score and Dynamic Baseline

At this point in the processing the data set contains intermingledscored values for both clustered outcomes and dynamic baseline data.

19. Re-calculate the dynamic baseline Set “BD” as above for use as acomparison source. Set “BD” is processed and scored. This step is notnecessary unless (as shown) one wants to implement logic such as“discard any outcomes sets where the baseline data scores higher thanthe sub-cluster”. Note that some of these sets (e.g., “B” and “D”) canbe written to use alternate methods of holding the processed data forlater use in the query. Some of these include global temp tables, WITHtemp AS, and other constructs that can pool sets into memory/disk can beused to curtail the reprocessing of the baseline sets.

20. Filter out rows in Set “ABCD” that have scores below 3.

21. Remove clustered outcome rows with scores >=3 if the dynamicbaseline score is equal to or exceeds the clustered outcome score for agiven outcome. The following code section is optional and serves thepurpose of tagging the attributes and outcomes with descriptiveinformation, showing only scores with an absolute values 3 or greater,and discarding outcome sets that have a lower score than their baselinecounterparts.

) d, ALL_CODE_XWALK a,ALL_CODE_XWALK o ... WHERE nd>3 AND nc>3 AND(xd+xc)>3 AND ((nc+nd)−(xc+xd))>3 ANDPOWER(ABS(xd−(xd+xc)*nd/(nd+nc))−.5,2) >=(8*(xd+xc)*nd/(nd+nc)*(1−nd/(nd+nc))) -- purposeful Cartesian since thetop IV returns one row )) c -- one of three base score tables for filterWHERE SUBSTR(d.attribute,1,2)=a.code_type(+) ANDd.attribute_value=a.code_set(+) ANDSUBSTR(d.outcome_class,1,2)=o.code_type(+) ANDd.outcome_type=o.code_set(+) AND d.outcome_class= c.outcome_class ANDd.outcome_type=c.outcome_type AND ABS(CASE WHEN cumul_hyper>1e−128 THENscore_sign*TRUNC(LOG(10,cumul_hyper)) ELSE −10*score_signEND)>ABS(c.score) AND ABS(CASE WHEN cumul_hyper>1e−128 THENscore_sign*TRUNC(LOG(10,cumul_hyper)) ELSE −10*score_sign END)>=3

Example 2

The “master sql” in Example 3 accesses SSTs and combines them intoattribute/outcome population sets. It then processes these sets intohypergeometric scores, relative risk and Yates confidence intervals asshown in steps 1 through 21 above.

Example 4 creates the SSTs for use by the master SQL. These SSTs arepacked by newind_id so the population set in play can be quicklyretrieved. This is accomplished for cluster_baseline_pops by the SQL.

SELECT DISTINCT

base.product_id,base.version_id,base.newindv_id,base.groupflag,base.attribute,base.attribute_value FROM

(merged sets)

The first two terms (product_id, version_id) land the data in apartition and the newindv_id field then clusters the data by individual.This SST packing could also be accomplished by using an ORDER BY clause,group by clause, a large concatenated index containing all the terms, orby using an index organized table.

Below, aspects of this example are explained in more detail. Let's stepthrough some of the nuances in the creation of the CLUSTER_BASELINE_POPStable. This table is used to supply counts of the population in anyattribute set and via the SST structure will quickly provide processingcode with the desired individual's attributes for rapid counting.

INSERT /*+append*/ INTO CLUSTER_BASELINE_POPS SELECT DISTINCTbase.product_id,base.version_id,base.newindv_id,base.groupflag,base.attribute,base.attribute_value FROM >>>>>This section identifies and tagsindividuals that were active at least >>>>>one day in the outcome period(the vast majority). The tag value ‘1 to >>>>>7 days’ will >>>>>be usedas an independent attribute and treated in >>>>>the code as if were a“typical” attribute like gender, age, taking drug >>>>>“A”, etc. duringthe baseline period. SELECTproduct_id,version_id,b.NEWINDV_ID,b.groupflag,‘Days since firstdispensing’ attribute, ‘1 to 7 days’ attribute_value,1 beginning,7ending FROM COHORT b WHERE b.match=1 AND b.termdate-b.indexdt >=1 ANDproduct_id=1 AND version_id=200503 UNION >>>>> We'll append on anothersection for those active at least 8 days into >>>>> the outcome period.etc for other days into segments SELECTproduct_id,version_id,b.NEWINDV_ID,b.groupflag,‘Days since firstdispensing’ attribute, ‘8 to 29 days’ attribute_value,8 beginning,29ending FROM COHORT b WHERE b.match=1 AND b.termdate-b.indexdt >=8 ANDproduct_id=1 AND version_id=200503 >>>>> Now we'll populate the genderattribute holding “Male or Female” in >>>>> the attribute_value SELECTproduct_id,version_id,NEWINDV_ID,groupflag,‘GENDER’ attribute,sexattribute_value ,0 beginning,99999 ending FROM COHORT WHERE match=1 ANDproduct_id=1 AND version_id=200503 UNION >>>>> Likewise for age orage_group (age can be grouped into logical ranges >>>>> as desired),SELECT product_id,version_id,NEWINDV_ID,groupflag,‘AGE_GROUP’attribute,b.AGE_GROUP_name attribute_value ,0 beginning,99999 endingFROM COHORT a, AGE_GROUP b WHERE a.AGE BETWEEN b.AGE_GROUP_START_YR ANDb.AGE_GROUP_END_YR AND a.match=1 AND product_id=1 AND version_id=200503UNION >>>>> Now codesets like diagnosis, procedure, prescription can betagged >>>>> (diagnosis shown). Medical code sets can be either actualcodes, >>>>> truncated codes, classes of codes, etc. There is nolimitation on how >>>>> an attribute can be classified. SELECTproduct_id,version_id,NEWINDV_ID,groupflag,‘DX Baseline’ attribute,dxattribute_value ,0 beginning,99999 ending FROM DXBASE WHERE match=1 ANDproduct_id=1 AND version_id=200503 AND indexflag=0 and drhlinpat=1 >>>>Note ANY attribute can be presented in this format including butnot >>>> limited to race, ethnicity, genetic information, familyhistory, >>>> height, weight, profession, smoker, etc.

Let's step through some of the nuances in the creation of theCLUSTER_XD_XC_PREJOIN_DX_OP table (The other outcome tables aresimilar). The purpose of this table is to permeate all possibleattribute with all possible outcomes individual by individual. Eachattribute/outcome pair is tagged by an individual id. This allows forcomplete flexibility when including or excluding sets of individuals byany desired criteria (single or multiple).

>>>> again we are creating an SST using the distinct clause which willsort >>>> by field order. The first two fields in this case will definea >>>> partition and the newindv_id will cluster data into the blocksby >>>> individual. INSERT /*+ append*/ INTO CLUSTER_XD_XC_PREJOIN_DX_OPSELECT DISTINCTbase.product_id,base.version_id,base.newindv_id,base.groupflag,base.attribute,base.attribute_value,emergent.outcome_class,emergent.outcome_type,emergent.days_in_study FROM >>>> Outcomes will be expressed in terms ofbase attributes so this table >>>> will fist find attributes much likewhat was done in >>>> cluster_baseline_pops. However this time we willtag each >>>> attribute/outcome pair with an individual_id instead ofjust each >>>> attribute with an individual_id. >>>> This section isfinding the outcomes for each person based on when the >>>> outcomeoccurred. Each persons attribute are then permeated by that >>>>person's outcomes via the join“base.newindv_id=emergent.newindv_id” >>>> and placed in the appropriatetime range “emergent.days_in_study BETWEEN >>>> base.beginning ANDbase.ending”. Note in this example the Dx codes are >>>> truncated towiden the outcome group. This is optional depending on the >>>> desiredoutcome granularity. Also we only use matched patients to >>>> mitigateconfounding of the results where each person in the study set >>>>requires a pair sharing key attribute characteristics. (SELECT DISTINCTproduct_id, version_id, a.newindv_id, a.groupflag, ‘DX_OUTPATIENT’outcome_class, CASE WHEN LENGTH (TRIM (a.dx_emerg)) = 4 AND SUBSTR(dx_emerg, 1, 1) <> ‘E’ THEN SUBSTR (dx_emerg, 1, 3) ELSE TRIM(dx_emerg) END outcome_type, days_in_study FROM dxemerg a WHERE a.match= 1 AND a.product_id=1 AND a.version_id = 200503 AND siteflag = 2 )emergent WHERE base.newindv_id=emergent.newindv_id ANDemergent.days_in_study BETWEEN base.beginning AND base.ending;

As described earlier, these tables can be used to quickly process and“score”

massive permutation sets of attribute/outcome pairs. Statisticallyinteresting combinations can easily by floated to the top by sorting onscore. Coupled with the filtering capability to select anysub-population this question can be extended into:

Are women who are on thyroid medications more likely to have headacheswhen taking drug “A” than drug “B”?

Are males between 50 and 59 who have had bypass surgery more likely tohave strokes when taking drug “A” than drug “B”

Does drug “B” have an unforeseen benefit in reducing a common disease inwomen who live in the south (e.g. Sinusitis)?

This powerful technique can also be extended to attribute sub clustersand outcome pairs (AKA syndromes). In other words we can permeate everycombination of attributes and find these important pieces of informationand float them to the top automatically. Also, pair of outcomes can beautomatically coupled into syndromes and mined. This sub-attributeclustering can also be coupled with syndromes to mine and score forinformation as complex as:

Women taking NSAIDs are more likely to show headache and vomiting whentaking drug “B” vs drug “A”. In other words, with no filtering criteriathe previously mentioned attribute/outcome pairs would automaticallyscore the following sets.

Set#1 Drug “A” vs. Drug “B”—Women experiencing headache

Set#2 Drug “A” vs. Drug “B”—Women experiencing vomiting

Set#3 Drug “A” vs. Drug “B”—All on NSAIDs experiencing headache

Set#4 Drug “A” vs. Drug “B”—All on NSAIDs experiencing vomiting

The sub-cluster sets and syndrome processing would also test, score andpresent:

Set#5 Drug “A” vs. Drug “B”—Women on NSAIDs experiencing headache

Set#6 Drug “A” vs. Drug “B”—Women on NSAIDs experiencing vomiting

Set#7 Drug “A” vs. Drug “B”—Women on NSAIDs experiencing headache andvomiting

Set#8 Drug “A” vs. Drug “B”—Women experiencing headache and vomiting

Set#9 Drug “A” vs. Drug “B”—All on NSAIDs experiencing headache andvomiting

Set#10 Drug “A” vs. Drug “B”—Women experiencing headache and vomiting

The example below walks through the generation of set#5 and set#6 (andall other possible permutations of attribute1/attribute2/outcometriplets)

By sourcing the CLUSTER_BASELINE_POPS table twice, one can create andstore all attribute sub-cluster permutations as shown below into table

ALLPROD2b_DOUBLET_NS. This table contains the attribute/attributecounts.

CREATE TABLE ALL_PROD2b_DOUBLET_NS AS SELECT a.ATTRIBUTE a1,a.ATTRIBUTE_VALUE av1,b.ATTRIBUTE a2, b.ATTRIBUTE_VALUE av2,COUNT(UNIQUE CASE WHEN a.groupflag=1 THEN a.newindv_id ELSE NULL END)nd ,COUNT(UNIQUE CASE WHEN a.groupflag=0 THEN a.newindv_id ELSE NULL END) nC FROM CLUSTER_BASELINE_POPS a,CLUSTER_BASELINE_POPS b WHEREa.product_id=2 AND b.product_id=2 AND /* a.ATTRIBUTE_VALUE=‘F’ ANDb.attribute_value=‘WEST’ AND */a.attribute∥a.attribute_value>b.attribute∥b.attribute_value ANDa.newindv_id=b.newindv_id /*AND a.attribute_value>b.attribute_valuedon't double return set*/ GROUP BY a.ATTRIBUTE ,a.ATTRIBUTE_VALUE,b.ATTRIBUTE, b.ATTRIBUTE_VALUE HAVING COUNT(UNIQUECASE WHEN a.groupflag=1 THEN a.newindv_id ELSE NULL END)>3 ANDCOUNT(UNIQUE CASE WHEN a.groupflag=0 THEN a.newindv_id ELSE NULL END )>3

Now we can create the complimentary table containing all the outcomescounts

for each attribute/attribute pair. This table contains theattribute/attribute outcome counts.

CREATE TABLE ALL_PROD2b_DOUBLET_XS SELECT * FROM ( SELECT a1,av1,a2,av2,outcome_class,outcome_type ,COUNT(UNIQUE CASE WHEN groupflag=1 THENnewindv_id ELSE NULL END) xd ,COUNT(UNIQUE CASE WHEN groupflag=0 THENnewindv_id ELSE NULL END ) xC FROM ( SELECT x.attributea1,x.attribute_value av1,y.attribute a2,y.attribute_valueav2,x.outcome_class,x.outcome_type,x.newindv_id,x.groupflag FROM (SELECT DISTINCTa.ATTRIBUTE,a.ATTRIBUTE_VALUE,a.outcome_class,a.outcome_type,newindv_id,groupflag FROM CLUSTER_XD_XC_PREJOIN_DX_OP a WHEREa.PRODUCT_ID=2 ) x, ( SELECT DISTINCTa.ATTRIBUTE,a.ATTRIBUTE_VALUE,a.outcome_class,a.outcome_type,newindv_id, groupflag FROM CLUSTER_XD_XC_PREJOIN_DX_OP a WHERE a.PRODUCT_ID=2) y WHERE x.attribute||x.attribute_value>y.attribute||y.attribute_valueAND x.newindv_id=y.newindv_id /* don't double return set*/ ANDx.outcome_class=y.outcome_class AND x.outcome_type=y.outcome_type )GROUP BY a1,av1,a2,av2 ,outcome_class,outcome_type )

These two tables can then be combined and scored often yielding powerfulinsight into hidden attribute combinations that have interestingproperties.

CREATE TABLE spir_dx_ip_doublet_scores AS SELECT iv2.*, CASE WHENPOWER(ABS(xd−(xd+xc)*nd/(nd+nc))−.5,2) >=(4*(xd+xc)*nd/(nd+nc)*(1−nd/(nd+nc))) THENWu4_Function9biot(x_prime,n_prime,xd+xc,nd+nc) ELSE 1 END cumul_hyperFROM ( SELECT iv.*,a.nd,a.nc ,CASE WHEN xd>(xd+xc)*nd/(nd+nc) THEN xcELSE xd END x_prime ,CASE WHEN xd>(xd+xc)*nd/(nd+nc) THEN nc ELSE nd ENDn_prime ,CASE WHEN xd>(xd+xc)*nd/(nd+nc) THEN −1 ELSE CASE WHEN xd=(xd+xc)*nd/(nd+nc) THEN 0 ELSE 1 END END score_sign FROMALL_PROD2b_DOUBLET_XS iv,ALL_PROD2b_DOUBLET_NS a WHERE iv.a1=a.a1 ANDiv.a2=a.a2 AND iv.av1=a.av1 AND iv.av2=a.av2 AND (xd+xc)>3 AND nd>3 ANDnc>3 AND ((nc + nd) − (xc + xd)) > 3 ANDPOWER(ABS(xd−(xd+xc)*nd/(nd+nc))−.5,2) >=(4*(xd+xc)*nd/(nd+nc)*(1−nd/(nd+nc))) ) iv2 ORDER BY cumul_hyper ASC

Example 3 Master Sql, Including Hypergeometric Call

SELECT * FROM ( SELECT 2 product_id,200503 version_id,d.attribute,d.attribute_value,a.code_desc “Attribute valuedesc”,d.outcome_class,d.outcome_type,o.code_desc “Outcome typedesc”,d.xd,d.nd,d.xc,d.nc, CASE WHEN d.xc>0 THEN TO_CHAR(d.xd*d.nc/(d.xc*d.nd),‘9999990.9’) ELSE ‘--’ END rr, CASE WHEN(x−plower*x)>0 THEN (plower*x/d.nd)/((x−plower*x)/d.nc) ELSE 0 ENDrrlower, CASE WHEN (x−pupper*x)>0 THEN(pupper*x/d.nd)/((x−pupper*x)/d.nc) ELSE 0 END rrupper, CASE WHENcumul_hyper>1e−128 THEN score_sign*TRUNC(LOG(10,d.cumul_hyper)) ELSE−10*score_sign END score FROM ( SELECTc.*,(−b_high+SQRT(POWER(b_high,2)−4*a*c_high))/(2*a)pupper,(−b_low−SQRT(POWER(b_low,2)−4*a*c_low))/(2*a) plower, CASE WHENPOWER(ABS(xd−(xd+xc)*nd/(nd+nc))−.5,2) >=(8*(xd+xc)*nd/(nd+nc)*(1−nd/(nd+nc))) THENaperio.Wu4_Function9biot(x_prime,n_prime,xd+xc,nd+nc) ELSE 1 ENDcumul_hyper FROM ( SELECT b.*,1+d a,−2*pd_low−d b_low,−2*pd_high−db_high,POWER(pd_low,2) c_low,POWER(pd_high,2) c_high FROM ( SELECTa.*,xd+xc x,POWER(1.96,2)/(xd+xc) d,(Xd−.5)/(xd+xc)pd_low,(Xd+.5)/(xd+xc) pd_high FROM ( SELECTnd_nc.attribute,nd_nc.attribute_value,xd_xc.outcome_class,xd_xc.outcome_(—)type,xd_xc.xd,nd_nc.nd,xd_xc.xc,nd_nc.nc ,CASE WHENxd>(xd+xc)*nd/(nd+nc) THEN xc ELSE xd END x_prime ,CASE WHENxd>(xd+xc)*nd/(nd+nc) THEN nc ELSE nd END n_prime ,CASE WHENxd>(xd+xc)*nd/(nd+nc) THEN −1 ELSE CASE WHEN xd= (xd+xc)*nd/(nd+nc) THEN0 ELSE 1 END END score_sign FROM ( SELECT attribute,attribute_value,COUNT (UNIQUE CASE WHEN groupflag=1 THEN newindv_id ELSE NULL END) nd,COUNT (UNIQUE CASE WHEN groupflag=0 THEN newindv_id ELSE NULL END) ncFROM CLUSTER_BASELINE_POPS WHERE product_id=2 AND version_id=200503 --Attach filter here AND newindv_id IN ( ) GROUP BYattribute,attribute_value UNION SELECT attribute,attribute_value,SUM(nd)nd,SUM(nc) nc FROM ( SELECT ‘Baseline’ attribute,‘’ attribute_value,COUNT (UNIQUE CASE WHEN groupflag=1 THEN newindv_id ELSE NULL END) nd,COUNT (UNIQUE CASE WHEN groupflag=0 THEN newindv_id ELSE NULL END) ncFROM CLUSTER_BASELINE_POPS WHERE product_id=2 AND version_id=200503 ANDattribute=‘GENDER’ -- Attach filter here AND newindv_id IN ( ) GROUP BYattribute,attribute_value ) GROUP BY attribute,attribute_value ) nd_nc ,( SELECT attribute,attribute_value,outcome_class,outcome_type ,COUNT(UNIQUE CASE WHEN groupflag=1 THEN newindv_id ELSE NULL END) xd ,COUNT(UNIQUE CASE WHEN groupflag=0 THEN newindv_id ELSE NULL END) xc FROMCLUSTER_XD_XC_PREJOIN_DX_OP -- one of three outcome tables WHEREproduct_id=2 AND version_id=200503 -- Attach filter here AND newindv_idIN ( ) GROUP BY attribute,attribute_value,outcome_class,outcome_typeUNION SELECT attribute,attribute_value,outcome_class,outcome_type,SUM(xd) xd,SUM(xc) xc FROM (SELECT ‘Baseline’ attribute,‘’attribute_value,outcome_class,outcome_type ,COUNT (UNIQUE CASE WHENgroupflag=1 THEN newindv_id ELSE NULL END) xd ,COUNT (UNIQUE CASE WHENgroupflag=0 THEN newindv_id ELSE NULL END) xc FROMCLUSTER_XD_XC_PREJOIN_DX_OP -- one of three outcome tables WHEREproduct_id=2 AND version_id=200503 AND attribute=‘GENDER’ -- Attachfilter here AND newindv_id IN ( ) GROUP BYattribute,attribute_value,outcome_class,outcome_type ) GROUP BYattribute, attribute_value,outcome_class,outcome_type ) xd_xc WHEREnd_nc.attribute=xd_xc.attribute ANDnd_nc.attribute_value=xd_xc.attribute_value AND nd>3 AND nc>3 AND(xd+xc)>3 AND ((nc+nd)−(xc+xd))>3 ) a ) b ) c ) d, ALL_CODE_XWALKa,ALL_CODE_XWALK o ( SELECT outcome_class,outcome_type,CASE WHENPOWER(ABS(xd−(xd+xc)*nd/(nd+nc))−.5,2) >=(8*(xd+xc)*nd/(nd+nc)*(1−nd/(nd+nc))) THENscore_sign*TRUNC(LOG(10,GREATEST(aperio.Wu4_Function9biot(x_prime,n_prime,xd+xc,nd+nc), 1e−128))) ELSE 0 END score FROM (SELECTnd_nc.attribute,nd_nc.attribute_value,xd_xc.outcome_class,xd_xc.outcome_(—)type,xd_xc.xd.nd_nc.nd,xd_xc.xc,nd_nc.nc ,CASE WHENxd>(xd+xc)*nd/(nd+nc) THEN xc ELSE xd END x_prime ,CASE WHENxd>(xd+xc)*nd/(nd+nc) THEN nc ELSE nd END n_prime ,CASE WHENxd>(xd+xc)*nd/(nd+nc) THEN −1 ELSE CASE WHEN xd= (xd+xc)*nd/(nd+nc) THEN0 ELSE 1 END END score_sign FROM ( SELECTattribute,attribute_value,SUM(nd) nd,SUM(nc) nc FROM ( SELECT ‘Baseline’attribute,‘’ attribute_value ,COUNT (UNIQUE CASE WHEN groupflag=1 THENnewindv_id ELSE NULL END) nd ,COUNT (UNIQUE CASE WHEN groupflag=0 THENnewindv_id ELSE NULL END) nc FROM CLUSTER_BASELINE_POPS WHEREproduct_id=2 AND version_id=200503 AND attribute=‘GENDER’ -- Attachfilter here AND newindv_id IN ( ) GROUP BY attribute,attribute_value )GROUP BY attribute,attribute_value ) nd_nc , ( SELECT attribute,attribute_value,outcome_class,outcome_type,SUM(xd) xd,SUM(xc) xc FROM (SELECT ‘Baseline’ attribute,‘’attribute_value,outcome_class,outcome_type ,COUNT (UNIQUE CASE WHENgroupflag=1 THEN newindv_id ELSE NULL END) xd ,COUNT (UNIQUE CASE WHENgroupflag=0 THEN newindv_id ELSE NULL END) xc FROMCLUSTER_XD_XC_PREJOIN_DX_OP -- one of three outcome tables WHEREproduct_id=2 AND version_id=200503 AND attribute=‘GENDER’ -- Attachfilter here AND newindv_id IN ( ) GROUP BYattribute,attribute_value,outcome_class,outcome_type ) GROUP BYattribute, attribute_value,outcome_class,outcome_type ) xd_xc WHERE nd>3AND nc>3 AND (xd+xc)>3 AND ((nc+nd)−(xc+xd))>3 ANDPOWER(ABS(xd−(xd+xc)*nd/(nd+nc))−.5,2) >=(8*(xd+xc)*nd/(nd+nc)*(1−nd/(nd+nc))) -- purposeful cartesian since thetop IV returns one row )) c -- one of three base score tables for filterWHERE SUBSTR(d.attribute,1,2)=a.code_type(+) ANDd.attribute_value=a.code_set(+) ANDSUBSTR(d.outcome_class,1,2)=o.code_type(+) ANDd.outcome_type=o.code_set(+) AND d.outcome_class=c.outcome_class ANDd.outcome_type=c.outcome_type AND ABS(CASE WHEN cumul_hyper>1e−128 THENscore_sign*TRUNC(LOG(10,cumul_hyper)) ELSE −10*score_signEND)>ABS(c.score) AND ABS(CASE WHEN cumul_hyper>1e−128 THENscore_sign*TRUNC(LOG(10,cumul_hyper)) ELSE −10*score_sign END)>=3

Example 4 Filename: r4product_id1_opiprx.sql

SELECT ‘DONE 1’ FROM DUAL;

Example 5

Techniques of this disclosure address a number of challenges in datamanipulation encountered in both the medical space and other areas.

Data Acquisition

All Attributes and outcomes may be stored in a coded manner that allowscomplete flexibility to accommodate all manner of terms (e.g.,attributes like “Male”, “Valium” and “Age” can occupy the same attributefield and be processed identically). SSTs provider rapid working sets ofdata, reducing physical I/Os by presenting the data in a “rich” formatwhere each database block holds many rows of the desired data. Forcluster investigations, SSTs can be created that hold all possibleattribute/outcome permutation sets. Sub-clusters (double attributes) andsyndromes (double outcomes) can also be processed if desired.

Data Reduction

Logarithms may be used in both the generation of the initial recursionpoint (logarithmic factorial table) and in the recurring of successiveterms (to avoid over/under flow). Attribute/outcome sets are quicklymingled and counted using the pre-permeated tables. Uninteresting casesmay be filtered out prior to processing if desired.

Data Presentation

Scores sets can be presented to the user in order of signal strength.Geographic charts can be used to provider “bird's eye” views of diseaseclusters, provider clusters, and the trending of both.

Problem (in order of explanation) Example Solution Attribute mixed formsCreate attribute table that allow co- (Male and DX = 250) mingling ofall possible attribute types Physical Disk I/Os required Packed tablescontaining only the fields for data retrieval desired (DB blocks are“rich” and field- efficient) Hash Join of attribute/ Pre-permeated (andpacked) outcomes attribute/outcome table sets Large number ofpermutation Elimination of uninteresting sets prior to sets processing(per Walker input) Hypergeometric: Work entirely in logarithms NumericOver/under flow Hypergeometric: Factorial Pre-generate, cached table ofall factorials generation (values held in LOG form, 4 cached I/Os tocreate initial recursion point)

Example 6 Alternate Coding Inline Views Vs. Set Based

Though SQL set based operations (MINUS, UNION, INTERSECT) are fast, itis possible to realize even faster results if the set variables arelimited to only those needed for the set based operation. For example,if one is interested in the gender distribution of patients withdiabetes who are also on LIPITOR but have not had coronary bypasssurgery in the last year, one can construct the SQL as below. A keypoint is that, in such an example, the “individual_id” is all that isrequired for the set intersection while “gender” is a piggyback variabledefined as useful in segmenting the counts (e.g. male and female below).When the SQL is coded in this way, the database is forced to hash bothpatient_id and gender when checking for individuals in both sets. Genderis redundant and not needed for this particular comparison operation.After the comparisons are made, gender is need to count individuals inthe various segments.

SELECT COUNT(UNIQUE a.individual_id) -- Set based (MINUS/INTERSECT),count(unique case when gender=‘M’ then individual_id else null end)males ,count(unique case when gender=‘F’ then individual_id else nullend) females FROM ( Select individual_id, gender from attribute_tablewhere attribute=‘DIABETES’ INTERSECT -- DB forced to process gender hereSelect individual_id, gender from attribute_table whereattribute=‘LIPITOR’ MINUS -- DB forced to process gender here Selectindividual_id, gender from attribute_table where attribute=‘CABG’ ) If,however, the SQL is coded with inline views as below, the database doesnot have to match “gender” when determining valid individuals i.e. onlyusing “gender” in the final segmentation case statements. The predicate“WHERE iv1.inidividual_id=iv2.individual_id” is more quickly doing the(above) INTERSECT and the “WHERE NOT EXISTS...” is more quickly doingthe (above) MINUS operation. SELECT COUNT(UNIQUE a.individual_id) -- Setbased (MINUS/INTERSECT) ,count(unique case when gender=‘M’ thenindividual_id else null end) males ,count(unique case when gender=‘F’then individual_id else null end) females FROM ( Selectiv1.individual_id,iv1.gender FROM (Select individual_id, gender fromattribute_table where attribute=‘DIABETES’ )iv1 ,(Select individual_id,gender from attribute_table where attribute=‘LIPITOR’) iv2 WHEREiv1.inidividual_id=iv2.individual_id ) iv12 WHERE NOT EXISTS SELECT ‘X’FROM attribute_table z WHERE z.attribute=‘CABG’ andz.individual_id=iv12.individual_id )

In practice the two approaches might appear as below. The inline viewversion gives the same results as the set based SQL and, in thisexample, returns the data four times faster than the set based SQL.Performance is likely to be even more pronounced between the twoapproaches if more piggyback variables are in play.

SELECT COUNT(UNIQUE a.individual_id) -- Set based (MINUS/INTERSECT),count(unique case when gender=‘M’ then individual_id else null end)males ,count(unique case when gender=‘F’ then individual_id else nullend) females ,count(unique case when gender not in (‘F’,‘M’) thenindividual_id else null end) unknown FROM ( SELECT a.INDIVIDUAL_ID,a.DOB, a.GENDER, a.ZIP5, SUBSTR(a.ZIP5, 1, 3) ZIP3 FROM INDIVIDUAL_ID aWHERE a.CODE_KEY IN(‘DX5053481’,‘DX505348481’,‘DX50534848481’,‘DX50534848491’,‘DX505348491’,‘DX505348511’,‘DX50534851481’,‘DX50534851491’,‘DX505348551’,‘DX505348561’,‘DX505348571’,‘DX505351531’,‘DX515355501’,‘DX515450481’,‘DX51545048491’,‘DX51545452491’,‘DX535656491’,‘DX545256481’,‘DX54525648481’,‘DX54525648491’,‘DX54525648501’,‘DX54525648511’,‘DX54525648521’,‘DX555553481’,‘DX555553491’,‘DX864956481’,‘DX865555491’,‘PX65535348481’,‘PX65535348491’,‘PX65535348501’,‘PX65535348511’,‘PX65535348521’,‘PX65535348531’,‘PX65535348541’,‘PX65535348551’,‘PX65535348561’,‘PX71484948561’,‘PX71484948571’,‘PX83574952481’,‘PX83574952491’,‘PX83575253531’,‘PX83575254481’,‘PX83575254531’,‘PX89525451481’)INTERSECT SELECT a.INDIVIDUAL_ID, a.DOB, a.GENDER, a.ZIP5,SUBSTR(a.ZIP5, 1, 3) ZIP3 FROM INDIVIDUAL_ID a WHERE a.CODE_KEY IN(‘RX-7101552337652’, ‘RX- 710155341’, ‘RX-7101554037652’,‘RX-7101562337652’, ‘RX-7101564037652’,‘RX-7101572337652’,‘RX-710157731’,‘RX-7101582337652’,‘RX-710158731’) MINUSSELECT a.INDIVIDUAL_ID, a.DOB, a.GENDER, a.ZIP5, SUBSTR(a.ZIP5, 1, 3)ZIP3 FROM INDIVIDUAL_ID a WHERE a.CODE_KEY IN(‘PX48485354541’,‘PX51515349481’,‘PX51515349491’,‘PX51515349501’,‘PX51515349511’,‘PX51515349521’,‘PX51515349541’,‘PX51515349551’,‘PX51515349561’,‘PX51515349571’,‘PX51515350491’,‘PX51515350501’,‘PX51515350511’,‘PX51515351511’,‘PX51515351521’,‘PX51515351531’,‘PX51515351541’) ) a SELECTCOUNT(UNIQUE individual_id) -- Inline View based ,count(unique case whengender=‘M’ then individual_id else null end) males ,count(unique casewhen gender=‘F’ then individual_id else null end) females ,count(uniquecase when gender not in (‘F’,‘M’) then individual_id else null end)unknown FROM ( SELECT a.individual_id,a.gender FROM ( SELECTa.INDIVIDUAL_ID, a.DOB, a.GENDER, a.ZIP5, SUBSTR(a.ZIP5, 1, 3) ZIP3 FROMINDIVIDUAL_ID a WHERE a.CODE_KEY IN(‘DX5053481’,‘DX505348481’,‘DX50534848481’,‘DX50534848491’,‘DX505348491’,‘DX505348511’,‘DX50534851481’,‘DX50534851491’,‘DX505348551’,‘DX505348561’,‘DX505348571’,‘DX505351531’,‘DX515355501’,‘DX515450481’,‘DX51545048491’,‘DX51545452491’,‘DX535656491’,‘DX545256481’,‘DX54525648481’,‘DX54525648491’,‘DX54525648501’,‘DX54525648511’,‘DX54525648521’,‘DX555553481’,‘DX555553491’,‘DX864956481’,‘DX865555491’,‘PX65535348481’,‘PX65535348491’,‘PX65535348501’,‘PX65535348511’,‘PX65535348521’,‘PX65535348531’,‘PX65535348541’,‘PX65535348551’,‘PX65535348561’,‘PX71484948561’,‘PX71484948571’,‘PX83574952481’,‘PX83574952491’,‘PX83575253531’,‘PX83575254481’,‘PX83575254531’,‘PX89525451481’) ) a, (SELECT a.INDIVIDUAL_ID, a.DOB, a.GENDER, a.ZIP5, SUBSTR(a.ZIP5, 1, 3)ZIP3 FROM INDIVIDUAL_ID a WHERE a.CODE_KEY IN (‘RX-7101552337652’, ‘RX-710155341’, ‘RX-7101554037652’, ‘RX-7101562337652’,‘RX-7101564037652’,‘RX-7101572337652’,‘RX-710157731’,‘RX-7101582337652’,‘RX-710158731’) ) bWHERE a.individual_id=b.individual_id ) d WHERE NOT EXISTS ( SELECT ‘X’FROM INDIVIDUAL_ID z WHERE z.CODE_KEY IN(‘PX48485354541’,‘PX51515349481’,‘PX51515349491’,‘PX51515349501’,‘PX51515349511’,‘PX51515349521’,‘PX51515349541’,‘PX51515349551’,‘PX51515349561’,‘PX51515349571’,‘PX51515350491’,‘PX51515350501’,‘PX51515350511’,‘PX51515351511’,‘PX51515351521’,‘PX51515351531’,‘PX51515351541’) ANDz.individual_id=d.individual_id);

With the benefit of the present disclosure, those having ordinary skillin the art will comprehend that techniques claimed here may be modifiedand applied to a number of additional, different applications, achievingthe same or a similar result. The claims cover all such modificationsthat fall within the scope and spirit of this disclosure.

REFERENCES

Each of the following references is hereby incorporated by reference inits entirety:

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What is claimed is:
 1. A computer program product, comprising: anon-transitory computer readable medium comprising: code to search data;code to compare one subset of the data against a plurality of othersubsets of the data; and code to calculate a hypergeometric statisticalresult based, at least in part, on the comparing step using one or morepre-generated factorial tables, the pre-generated factorial tablescomprising logarithmic entries.
 2. The computer program product of claim1, further comprising code to detect fraud based, at least in part, onthe calculated hypergeometric statistical result.
 3. The computerprogram product of claim 1, further comprising code to perform qualitycontrol on the data.
 4. The computer program product of claim 1, furthercomprising code to perform pattern recognition on the data.
 5. Thecomputer program product of claim 1, further comprising code to performcluster validity analysis on the data.
 6. The computer program productof claim 1, further comprising code to perform tree classifier design onthe data.
 7. The computer program product of claim 1, further comprisingcode to perform image template matching on the data.
 8. The computerprogram product of claim 1, further comprising code to cluster the datainto appropriate groupings before calculating the hypergeometricstatistical result.
 9. The computer program product of claim 1, furthercomprising code to present certain portions of the data to a user based,at least in part, on the calculated hypergeometric statistical result.10. The computer program product of claim 1, wherein the code tocalculate the hypergeometric statistical result comprises: code tocalculate a starting probability distribution function (pdf) value; andcode to recursively generate additional pdf values using the logarithmicentries.
 11. The computer program product of claim 1, wherein thelogarithmic entries comprise natural logarithmic entries.
 12. Thecomputer program product of claim 1, wherein the data comprisesspecialized search tables (SSTs), and the medium further comprises codeto generate the specialized search tables (SSTs) by selecting particulardata for inclusion in the specialized search tables (SSTs).
 13. A methodfor statistical calculations based on administrative health claims datacomprising: searching, with a computing device, data; comparing, withthe computing device, one subset of the data against a plurality ofother subsets of the data; and calculating, with the computing device, ahypergeometric statistical result based, at least in part, on thecomparing step using one or more pre-generated factorial tables, thepre-generated factorial tables comprising logarithmic entries.
 14. Themethod of claim 13, further comprising using the hypergeometricstatistical result to detect medical-related fraud.
 15. The method ofclaim 13, further comprising performing quality control on the data. 16.The method of claim 13, further comprising performing patternrecognition on the data.
 17. The method of claim 13, further comprisingperforming cluster validity analysis on the data.
 18. The method ofclaim 13, further comprising performing tree classifier design on thedata.
 19. The method of claim 13, further comprising performing imagetemplate matching on the data.
 20. The method of claim 13, furthercomprising clustering the data into appropriate groupings beforecalculating the hypergeometric statistical result.
 21. The method ofclaim 13, further comprising: calculating a starting probabilitydistribution function (pdf) value; and recursively generating additionalpdf values using the logarithmic entries.
 22. The method of claim 13,wherein the logarithmic entries comprise natural logarithmic entries.23. The method of claim 13, wherein the data comprises specializedsearch tables (SSTs), and the medium further comprises code to generatethe specialized search tables (SSTs) by selecting particular data forinclusion in the specialized search tables (SSTs).